From Certain to Uncertain: Toward Optimal Solution for Offline Multiple Object Tracking

Kaikai Zhao, Takashi Imaseki, Hiroshi Mouri, Einoshin Suzuki, Tetsu Matsukawa

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Auto-TLDR; Agglomerative Hierarchical Clustering with Ensemble of Tracking Experts for Object Tracking

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Affinity measure in object tracking outputs a similarity or distance score for given detections. As an affinity measure is typically imperfect, it generally has an uncertain region in which regarding two groups of detections as the same object or different objects based on the score can be wrong. How to reduce the uncertain region is a major challenge for most similarity-based tracking methods. Early mistakes often result in distribution drifts for tracked objects and this is another major issue for object tracking. In this paper, we propose a new offline tracking method called agglomerative hierarchical clustering with ensemble of tracking experts (AHC_ETE), to tackle the uncertain region and early mistake issues. We conduct tracking from certain to uncertain to reduce early mistakes. Meanwhile, we ensemble multiple tracking experts to reduce the uncertain region as the final one is the union of that of each tracking expert. Experiments on MOT16 datasets demonstrated the effectiveness of our method.

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Compact and Discriminative Multi-Object Tracking with Siamese CNNs

Claire Labit-Bonis, Jérôme Thomas, Frederic Lerasle

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Auto-TLDR; Fast, Light-Weight and All-in-One Single Object Tracking for Multi-Target Management

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Following the tracking-by-detection paradigm, multiple object tracking deals with challenging scenarios, occlusions or even missing detections; the priority is often given to quality measures instead of speed, and a good trade-off between the two is hard to achieve. Based on recent work, we propose a fast, light-weight tracker able to predict targets position and reidentify them at once, when it is usually done with two sequential steps. To do so, we combine a bounding box regressor with a target-oriented appearance learner in a newly designed and unified architecture. This way, our tracker can infer the targets' image pose but also provide us with a confidence level about target identity. Most of the time, it is also common to filter out the detector outputs with a preprocessing step, throwing away precious information about what has been seen in the image. We propose a tracks management strategy able to balance efficiently between detection and tracking outputs and their associated likelihoods. Simply put, we spotlight a full siamese based single object tracker able to predict both position and appearance features at once with a light-weight and all-in-one architecture, within a balanced overall multi-target management strategy. We demonstrate the efficiency and speed of our system w.r.t the literature on the well-known MOT17 challenge benchmark, and bring to the fore qualitative evaluations as well as state-of-the-art quantitative results.

SynDHN: Multi-Object Fish Tracker Trained on Synthetic Underwater Videos

Mygel Andrei Martija, Prospero Naval

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Auto-TLDR; Underwater Multi-Object Tracking in the Wild with Deep Hungarian Network

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In this paper, we seek to extend multi-object tracking research on a relatively less explored domain, that of, underwater multi-object tracking in the wild. Multi-object fish tracking is an important task because it can provide fish monitoring systems with richer information (e.g. multiple views of the same fish) as compared to detections and it can be an invaluable input to fish behavior analysis. However, there is a lack of an annotated benchmark dataset with enough samples for this task. To circumvent the need for manual ground truth tracking annotation, we craft a synthetic dataset. Using this synthetic dataset, we train an integrated detector and tracker called SynDHN. SynDHN uses the Deep Hungarian Network (DHN), which is a differentiable approximation of the Hungarian assignment algorithm. We repurpose DHN to become the tracking component of our algorithm by performing the task of affinity estimation between detector predictions. We consider both spatial and appearance features for affinity estimation. Our results show that despite being trained on a synthetic dataset, SynDHN generalizes well to real underwater video tracking and performs better against our baseline algorithms.

IPT: A Dataset for Identity Preserved Tracking in Closed Domains

Thomas Heitzinger, Martin Kampel

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Auto-TLDR; Identity Preserved Tracking Using Depth Data for Privacy and Privacy

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We present a public dataset for Identity Preserved Tracking (IPT) consisting of sequences of depth data recorded using an Orbbec Astra depth sensor. The dataset features sequences in ten different locations with a high amount of background variation and is designed to be applicable to a wide range of tasks. Its labeling is versatile, allowing for tracking in either 3d space or image coordinates. Next to frame-by-frame 3d and inferred bounding box labeling we provide supplementary annotation of camera poses and room layouts, split in multiple semantically distinct categories. Intended use-cases are applications where both a high level understanding of scene understanding and privacy are central points of consideration, such as active and assisted living (AAL), security and industrial safety. Compared to similar public datasets IPT distinguishes itself with its sequential data format, 3d instance labeling and room layout annotation. We present baseline object detection results in image coordinates using a YOLOv3 network architecture and implement a background model suitable for online tracking applications to increase detection accuracy. Additionally we propose a novel volumetric non-maximum suppression (V-NMS) approach, taking advantage of known room geometry. Last we provide baseline person tracking results utilizing Multiple Object Tracking Challenge (MOTChallenge) evaluation metrics of the CVPR19 benchmark.

An Adaptive Fusion Model Based on Kalman Filtering and LSTM for Fast Tracking of Road Signs

Chengliang Wang, Xin Xie, Chao Liao

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Auto-TLDR; Fusion of ThunderNet and Region Growing Detector for Road Sign Detection and Tracking

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The detection and tracking of road signs plays a critical role in various autopilot application. Utilizing convolutional neural networks(CNN) mostly incurs a big run-time overhead in feature extraction and object localization. Although Klaman filter(KF) is a commonly-used tracker, it is likely to be impacted by omitted objects in the detection step. In this paper, we designed a high-efficient detector that combines ThunderNet and Region Growing Detector(RGD) to detect road signs, and built a fusion model of long short term memory network (LSTM) and KF in the state estimation and the color histogram. The experimental results demonstrate that the proposed method improved the state estimation accuracy by 6.4% and enhanced the Frames Per Second(FPS) to 41.

Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation

Bhargava Urala Kota, Alexander Stone, Kenny Davila, Srirangaraj Setlur, Venu Govindaraju

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Auto-TLDR; A Framework for Summarizing Whiteboard Lecture Videos Using Feature Representations of Handwritten Content Regions

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Lecture videos are rapidly becoming an invaluable source of information for students across the globe. Given the large number of online courses currently available, it is important to condense the information within these videos into a compact yet representative summary that can be used for search-based applications. We propose a framework to summarize whiteboard lecture videos by finding feature representations of detected handwritten content regions to determine unique content. We investigate multi-scale histogram of gradients and embeddings from deep metric learning for feature representation. We explicitly handle occluded, growing and disappearing handwritten content. Our method is capable of producing two kinds of lecture video summaries - the unique regions themselves or so-called key content and keyframes (which contain all unique content in a video segment). We use weighted spatio-temporal conflict minimization to segment the lecture and produce keyframes from detected regions and features. We evaluate both types of summaries and find that we obtain state-of-the-art peformance in terms of number of summary keyframes while our unique content recall and precision are comparable to state-of-the-art.

Model Decay in Long-Term Tracking

Efstratios Gavves, Ran Tao, Deepak Gupta, Arnold Smeulders

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Auto-TLDR; Model Bias in Long-Term Tracking

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To account for appearance variations, tracking models need to be updated during the course of inference. However, updating the tracker model with adverse bounding box predictions adds an unavoidable bias term to the learning. This bias term, which we refer to as model decay, offsets the learning and causes tracking drift. While its adverse affect might not be visible in short-term tracking, accumulation of this bias over a long-term can eventually lead to a permanent loss of the target. In this paper, we look at the problem of model bias from a mathematical perspective. Further, we briefly examine the effect of various sources of tracking error on model decay, using a correlation filter (ECO) and a Siamese (SINT) tracker. Based on observations and insights, we propose simple additions that help to reduce model decay in long-term tracking. The proposed tracker is evaluated on four long-term and one short-term tracking benchmarks, demonstrating superior accuracy and robustness, even on 30 minute long videos.

Story Comparison for Estimating Field of View Overlap in a Video Collection

Thierry Malon, Sylvie Chambon, Alain Crouzil, Vincent Charvillat

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Auto-TLDR; Finding Videos with Overlapping Fields of View Using Video Data

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Determining the links between large amounts of video data with no prior knowledge of the camera positions is a hard task to automate. From a collection of videos acquired from static cameras simultaneously, we propose a method for finding groups of videos with overlapping fields of view. Each video is first processed individually: at regular time steps, objects are detected and are assigned a category and an appearance descriptor. Next, the video is split into cells at different resolutions and we assign to each cell its story: it consists of the list of objects detected in the cell over time. Once the stories are established for each video, the links between cells of different videos are determined by comparing their stories: two cells are linked if they show simultaneous detections of objects of the same category with similar appearances. Pairs of videos with overlapping fields of view are identified using these links between cells. A link graph is finally returned, in which each node represents a video, and the edges indicate pairs of overlapping videos. The approach is evaluated on a set of 63 real videos from both public datasets and live surveillance videos, as well as on 84 synthetic videos, and shows promising results.

Vehicle Lane Merge Visual Benchmark

Kai Cordes, Hellward Broszio

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Auto-TLDR; A Benchmark for Automated Cooperative Maneuvering Using Multi-view Video Streams and Ground Truth Vehicle Description

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Automated driving is regarded as the most promising technology for improving road safety in the future. In this context, connected vehicles have an important role regarding their ability to perform cooperative maneuvers for challenging traffic situations. We propose a benchmark for automated cooperative maneuvers. The targeted cooperative maneuver is the vehicle lane merge where a vehicle on the acceleration lane merges into the traffic of a motorway. The benchmark enables the evaluation of vehicle localization approaches as well as the study of cooperative maneuvers. It consists of temporally synchronized multi-view video streams, highly accurate camera calibration, and ground truth vehicle descriptions, including position, heading, speed, and shape. For benchmark generation, the lane merge maneuver is performed by human drivers on a test track, resulting in 120 lane merge data sets with various traffic situations and video recording conditions.

Graph-Based Image Decoding for Multiplexed in Situ RNA Detection

Gabriele Partel, Carolina Wahlby

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Auto-TLDR; A Graph-based Decoding Approach for Multiplexed In situ RNA Detection

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Image-based multiplexed in situ RNA detection makes it possible to map the spatial gene expression of hundreds to thousands of genes in parallel, and thus discern at the same time a large numbers of different cell types to better understand tissue development, heterogeneity, and disease. Fluorescent signals are detected over multiple fluorescent channels and imaging rounds and decoded in order to identify RNA molecules in their morphological context. Here we present a graph-based decoding approach that models the decoding process as a network flow problem jointly optimizing observation likelihoods and distances of signal detections, thus achieving robustness with respect to noise and spatial jitter of the fluorescent signals. We evaluated our method on synthetic data generated at different experimental conditions, and on real data of in situ RNA sequencing, comparing results with respect to alternative and gold standard image decoding pipelines.

Uncertainty Guided Recognition of Tiny Craters on the Moon

Thorsten Wilhelm, Christian Wöhler

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Auto-TLDR; Accurately Detecting Tiny Craters in Remote Sensed Images Using Deep Neural Networks

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Accurately detecting craters in remotely sensed images is an important task when analysing the properties of planetary bodies. Commonly, only large craters in the range of several kilometres are detected. In this work we provide the first example of automatically detecting tiny craters in the range of several meters with the help of a deep neural network by using only a small set of annotated craters. Additionally, we propose a novel way to group overlapping detections and replace the commonly used non-maximum suppression with a probabilistic treatment. As a result, we receive valuable uncertainty estimates of the detections and the aggregated detections are shown to be vastly superior.

AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features

Maximilian Kraus, Seyed Majid Azimi, Emec Ercelik, Reza Bahmanyar, Peter Reinartz, Alois Knoll

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Auto-TLDR; AerialMPTNet: A novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features

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Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large number and the tiny size of the pedestrians (e.g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e.g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well. In this paper, we propose AerialMPTNet, a novel approach for multi-pedestrian tracking in geo-referenced aerial imagery by fusing appearance features from a Siamese Neural Network, movement predictions from a Long Short-Term Memory, and pedestrian interconnections from a GraphCNN. In addition, to address the lack of diverse aerial multi-pedestrian tracking datasets, we introduce the Aerial Multi-Pedestrian Tracking (AerialMPT) dataset consisting of 307 frames and 44,740 pedestrians annotated. To the best of our knowledge, AerialMPT is the largest and most diverse dataset to this date and will be released publicly. We evaluate AerialMPTNet on AerialMPT and KIT AIS, and benchmark with several state-of-the-art tracking methods. Results indicate that AerialMPTNet significantly outperforms other methods on accuracy and time-efficiency.

FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings

Niels Ole Salscheider

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Auto-TLDR; FeatureNMS: Non-Maximum Suppression for Multiple Object Detection

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Most state of the art object detectors output multiple detections per object. The duplicates are removed in a post-processing step called Non-Maximum Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that contain objects with high overlap: The idea of this heuristic is that a high bounding box overlap corresponds to a high probability of having a duplicate. We propose FeatureNMS to solve this problem. FeatureNMS recognizes duplicates not only based on the intersection over union between bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance. Our approach outperforms classical NMS and derived approaches and achieves state of the art performance.

Correlation-Based ConvNet for Small Object Detection in Videos

Brais Bosquet, Manuel Mucientes, Victor Brea

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Auto-TLDR; STDnet-ST: An End-to-End Spatio-Temporal Convolutional Neural Network for Small Object Detection in Video

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The detection of small objects is of particular interest in many real applications. In this paper, we propose STDnet-ST, a novel approach to small object detection in video using spatial information operating alongside temporal video information. STDnet-ST is an end-to-end spatio-temporal convolutional neural network that detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing small objects. This architecture links the small objects across the time as tubelets, being able to dismiss unprofitable object links in order to provide high-quality tubelets. STDnet-ST achieves state-of-the-art results for small objects on the publicly available USC-GRAD-STDdb and UAVDT video datasets.

Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans

Avik Hati, Matteo Bustreo, Diego Sona, Vittorio Murino, Alessio Del Bue

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Auto-TLDR; A Weakly Supervised and Efficient Interactive Segmentation of Ancient Egyptian Mummies CT Scans Using Geodesic Distance Measure and GrabCut

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In this paper, we tackle the task of automatically analyzing 3D volumetric scans obtained from computed tomography (CT) devices. In particular, we address a particular task for which data is very limited: the segmentation of ancient Egyptian mummies CT scans. We aim at digitally unwrapping the mummy and identify different segments such as body, bandages and jewelry. The problem is complex because of the lack of annotated data for the different semantic regions to segment, thus discouraging the use of strongly supervised approaches. We, therefore, propose a weakly supervised and efficient interactive segmentation method to solve this challenging problem. After segmenting the wrapped mummy from its exterior region using histogram analysis and template matching, we first design a voxel distance measure to find an approximate solution for the body and bandage segments. Here, we use geodesic distances since voxel features as well as spatial relationship among voxels is incorporated in this measure. Next, we refine the solution using a GrabCut based segmentation together with a tracking method on the slices of the scan that assigns labels to different regions in the volume, using limited supervision in the form of scribbles drawn by the user. The efficiency of the proposed method is demonstrated using visualizations and validated through quantitative measures and qualitative unwrapping of the mummy.

Self-Paced Bottom-Up Clustering Network with Side Information for Person Re-Identification

Mingkun Li, Chun-Guang Li, Ruo-Pei Guo, Jun Guo

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Auto-TLDR; Self-Paced Bottom-up Clustering Network with Side Information for Unsupervised Person Re-identification

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Person re-identification (Re-ID) has attracted a lot of research attention in recent years. However, supervised methods demand an enormous amount of manually annotated data. In this paper, we propose a Self-Paced bottom-up Clustering Network with Side Information (SPCNet-SI) for unsupervised person Re-ID, where the side information comes from the serial number of the camera associated with each image. Specifically, our proposed SPCNet-SI exploits the camera side information to guide the feature learning and uses soft label in bottom-up clustering process, in which the camera association information is used in the repelled loss and the soft label based cluster information is used to select the candidate cluster pairs to merge. Moreover, a self-paced dynamic mechanism is developed to regularize the merging process such that the clustering is implemented in an easy-to-hard way with a slow-to-fast merging process. Experiments on two benchmark datasets Market-1501 and DukeMTMC-ReID demonstrate promising performance.

SiamMT: Real-Time Arbitrary Multi-Object Tracking

Lorenzo Vaquero, Manuel Mucientes, Victor Brea

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Auto-TLDR; SiamMT: A Deep-Learning-based Arbitrary Multi-Object Tracking System for Video

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Visual object tracking is of great interest in many applications, as it preserves the identity of an object throughout a video. However, while real applications demand systems capable of real-time-tracking multiple objects, multi-object tracking solutions usually follow the tracking-by-detection paradigm, thus they depend on running a costly detector in each frame, and they do not allow the tracking of arbitrary objects, i.e., they require training for specific classes. In response to this need, this work presents the architecture of SiamMT, a system capable of efficiently applying individual visual tracking techniques to multiple objects in real-time. This makes it the first deep-learning-based arbitrary multi-object tracker. To achieve this, we propose the global frame features extraction by using a fully-convolutional neural network, followed by the cropping and resizing of the different object search areas. The final similarity operation between these search areas and the target exemplars is carried out with an optimized pairwise cross-correlation. These novelties allow the system to track multiple targets in a scalable manner, achieving 25 fps with 60 simultaneous objects for VGA videos and 40 objects for HD720 videos, all with a tracking quality similar to SiamFC.

ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition

Samadhi Poornima Kumarasinghe Wickrama Arachchilage, Ebroul Izquierdo

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Auto-TLDR; Joint Clustering and Classification for Face Recognition in the Wild

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Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios 'on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.

Generic Merging of Structure from Motion Maps with a Low Memory Footprint

Gabrielle Flood, David Gillsjö, Patrik Persson, Anders Heyden, Kalle Åström

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Auto-TLDR; A Low-Memory Footprint Representation for Robust Map Merge

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With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged. This calls for development of ways to handle all these data. In this paper, we present new tools that will enable efficient, flexible and robust map merging. Assuming that separate optimisations have been performed for the individual maps, we show how only relevant data can be stored in a low memory footprint representation. We use these representations to perform map merging so that the algorithm is invariant to the merging order and independent of the choice of coordinate system. The result is a robust algorithm that can be applied to several maps simultaneously. The result of a merge can also be represented with the same type of low-memory footprint format, which enables further merging and updating of the map in a hierarchical way. Furthermore, the method can perform loop closing and also detect changes in the scene between the capture of the different image sequences. Using both simulated and real data — from both a hand held mobile phone and from a drone — we verify the performance of the proposed method.

Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks

Ning Zhang, Jingen Liu, Ke Wang, Dan Zeng, Tao Mei

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Auto-TLDR; Two-Stream Residual Convolutional Network for Visual Tracking

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The current deep learning based visual tracking approaches have been very successful by learning the target classification and/or estimation model from a large amount of supervised training data in offline mode. However, most of them can still fail in tracking objects due to some more challenging issues such as dense distractor objects, confusing background, motion blurs, and so on. Inspired by the human ``visual tracking'' capability which leverages motion cues to distinguish the target from the background, we propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking, which successfully exploits both appearance and motion features for model update. Our TS-RCN can be integrated with existing deep learning based visual trackers. To further improve the tracking performance, we adopt a ``wider'' residual network ResNeXt as its feature extraction backbone. To the best of our knowledge, TS-RCN is the first end-to-end trainable two-stream visual tracking system, which makes full use of both appearance and motion features of the target. We have extensively evaluated the TS-RCN on most widely used benchmark datasets including VOT2018, VOT2019, and GOT-10K. The experiment results have successfully demonstrated that our two-stream model can greatly outperform the appearance based tracker, and it also achieves state-of-the-art performance. The tracking system can run at up to 38.1 FPS.

Utilising Visual Attention Cues for Vehicle Detection and Tracking

Feiyan Hu, Venkatesh Gurram Munirathnam, Noel E O'Connor, Alan Smeaton, Suzanne Little

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Auto-TLDR; Visual Attention for Object Detection and Tracking in Driver-Assistance Systems

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Advanced Driver-Assistance Systems (ADAS) have been attracting attention from many researchers. Vision based sensors are the closest way to emulate human driver visual behavior while driving. In this paper, we explore possible ways to use visual attention (saliency) for object detection and tracking. We investigate: 1) How a visual attention map such as a subjectness attention or saliency map and an objectness attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects. We propose a neural network that can simultaneously detect objects as and generate objectness and subjectness maps to save computational power. We further exploit the visual attention map during tracking using a sequential Monte Carlo probability hypothesis density (PHD) filter. The experiments are conducted on KITTI and DETRAC datasets. The use of visual attention and hierarchical features has shown a considerable improvement of≈8% in object detection which effectively increased tracking performance by≈4% on KITTI dataset.

Online Object Recognition Using CNN-Based Algorithm on High-Speed Camera Imaging

Shigeaki Namiki, Keiko Yokoyama, Shoji Yachida, Takashi Shibata, Hiroyoshi Miyano, Masatoshi Ishikawa

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Auto-TLDR; Real-Time Object Recognition with High-Speed Camera Imaging with Population Data Clearing and Data Ensemble

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High-speed camera imaging (e.g., 1,000 fps) is effective to detect and recognize objects moving at high speeds because temporally dense images obtained by a high-speed camera can usually capture the best moment for object detection and recognition. However, the latest recognition algorithms, with their high complexity, are difficult to utilize in real-time applications involving high-speed cameras because a vast amount of images need to be processed with no latency. To tackle this problem, we propose a novel framework for real-time object recognition with high-speed camera imaging. The proposed framework has the key processes of population data cleansing and data ensemble. Population data cleansing improves the recognition accuracy by quantifying the recognizability and by excluding part of the images prior to the recognition process, while data ensemble improves the robustness of object recognition by merging the class probabilities with multiple images of the same object. Experimental results with a real dataset show that our framework is more effective than existing methods.

Open-World Group Retrieval with Ambiguity Removal: A Benchmark

Ling Mei, Jian-Huang Lai, Zhanxiang Feng, Xiaohua Xie

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Auto-TLDR; P2GSM-AR: Re-identifying changing groups of people under the open-world and group-ambiguity scenarios

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Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. This paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. The open-world scenario considers that there are probably non-target people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. The open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.

Multi-View Object Detection Using Epipolar Constraints within Cluttered X-Ray Security Imagery

Brian Kostadinov Shalon Isaac-Medina, Chris G. Willcocks, Toby Breckon

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Auto-TLDR; Exploiting Epipolar Constraints for Multi-View Object Detection in X-ray Security Images

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Automatic detection for threat object items is an increasing emerging area of future application in X-ray security imagery. Although modern X-ray security scanners can provide two or more views, the integration of such object detectors across the views has not been widely explored with rigour. Therefore, we investigate the application of geometric constraints using the epipolar nature of multi-view imagery to improve object detection performance. Furthermore, we assume that images come from uncalibrated views, such that a method to estimate the fundamental matrix using ground truth bounding box centroids from multiple view object detection labels is proposed. In addition, detections are given a score based on its similarity with respect to the distribution of the error of the epipolar estimation. This score is used as confidence weights for merging duplicated predictions using non-maximum suppression. Using a standard object detector (YOLOv3), our technique increases the average precision of detection by 2.8% on a dataset composed of firearms, laptops, knives and cameras. These results indicate that the integration of images at different views significantly improves the detection performance of threat items of cluttered X-ray security images.

Tracking Fast Moving Objects by Segmentation Network

Ales Zita, Filip Sroubek

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Auto-TLDR; Fast Moving Objects Tracking by Segmentation Using Deep Learning

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Tracking Fast Moving Objects (FMO), which appear as blurred streaks in video sequences, is a difficult task for standard trackers, as the object position does not overlap in consecutive video frames and texture information of the objects is blurred. Up-to-date approaches tuned for this task are based on background subtraction with a static background and slow deblurring algorithms. In this article, we present a tracking-by-segmentation approach implemented using modern deep learning methods that perform near real-time tracking on real-world video sequences. We have developed a physically plausible FMO sequence generator to be a robust foundation for our training pipeline and demonstrate straightforward network adaptation for different FMO scenarios with varying foreground.

Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Kalun Ho, Janis Keuper, Franz-Josef Pfreundt, Margret Keuper

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Auto-TLDR; Clustering Objectives for K-means and Correlation Clustering Using Triplet Loss

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In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.

Reducing False Positives in Object Tracking with Siamese Network

Takuya Ogawa, Takashi Shibata, Shoji Yachida, Toshinori Hosoi

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Auto-TLDR; Robust Long-Term Object Tracking with Adaptive Search based on Motion Models

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We have developed a robust long-term object tracking method that resolves the fundamental cause of the drift and loss of a target in visual object tracking. The proposed method consists of “sampling area extension”, which prevents a tracking result from drifting to other objects by learning false positive samples in advance (before they enter the search region of the target), and “adaptive search based on motion models”, which prevents a tracking result from drifting to other objects and avoids the loss of the target by using not only appearance features but also motion models to adaptively search for the target. Experiments conducted on long-term tracking dataset showed that our first technique improved robustness by 16.6% while the second technique improved robustness by 15.3%. By combining both, our method achieved 21.7% and 9.1% improvement for the robustness and precision, and the processing speed became 3.3 times faster. Additional experiments showed that our method achieved the top robustness among state-of-the-art methods on three long-term tracking datasets. These findings demonstrate that our method is effective for long-term object tracking and that its performance and speed are promising for use in practical applications of various technologies underlying object tracking.

Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification

Tetsu Matsukawa, Einoshin Suzuki

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Auto-TLDR; A small-scale CNN feature transfer method for person re-identification

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Modern Convolutional Neural Networks~(CNNs) have been improving the accuracy of person re-identification (re-id) using a large number of training samples. Such a re-id system suffers from a lack of training samples for deployment to practical security applications. To address this problem, we focus on the approach that transfers CNN features pre-trained on a large-scale person re-id dataset to a small-scale dataset. Most of the ordinal CNN feature transfer methods use the features of fully connected layers that entangle locally pooled features of different spatial locations on an image. Unfortunately, due to the difference of view angles and the bias of walking directions of the persons, each camera view in a dataset has a unique spatial property in the person image, which reduces the generality of the local pooling for different cameras/datasets. To account for the camera- and dataset-specific spatial bias, we propose a method to learn camera and dataset-specific position weight maps for discriminative local pooling of convolutional features. Our experiments on four public datasets confirm the effectiveness of the proposed feature transfer with a small number of training samples in the target datasets.

Siamese Fully Convolutional Tracker with Motion Correction

Mathew Francis, Prithwijit Guha

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Auto-TLDR; A Siamese Ensemble for Visual Tracking with Appearance and Motion Components

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Visual tracking algorithms use cues like appearance, structure, motion etc. for locating an object in a video. We propose an ensemble tracker with appearance and motion components. A siamese tracker that learns object appearance from a static image and motion vectors computed between consecutive frames with a flow network forms the ensemble. Motion predicted object localization is used to correct the appearance component in the ensemble. Complementary nature of the components bring performance improvement as observed in experiments performed on VOT2018 and VOT2019 datasets.

3D Pots Configuration System by Optimizing Over Geometric Constraints

Jae Eun Kim, Muhammad Zeeshan Arshad, Seong Jong Yoo, Je Hyeong Hong, Jinwook Kim, Young Min Kim

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Auto-TLDR; Optimizing 3D Configurations for Stable Pottery Restoration from irregular and noisy evidence

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While potteries are common artifacts excavated in archaeological sites, the restoration process relies on the manual cleaning and reassembling shattered pieces. Since the number of possible 3D configurations is considerably large, the exhaustive manual trial may result in an abrasion on fractured surfaces and even failure to find the correct matches. As a result, many recent works suggest virtual reassembly from 3D scans of the fragments. The problem is challenging in the view of the conventional 3D geometric analysis, as it is hard to extract reliable shape features from the thin break lines. We propose to optimize the global configuration by combining geometric constraints with information from noisy shape features. Specifically, we enforce bijection and continuity of sequence of correspondences given estimates of corners and pair-wise matching scores between multiple break lines. We demonstrate that our pipeline greatly increases the accuracy of correspondences, resulting in the stable restoration of 3D configurations from irregular and noisy evidence.

DAL: A Deep Depth-Aware Long-Term Tracker

Yanlin Qian, Song Yan, Alan Lukežič, Matej Kristan, Joni-Kristian Kamarainen, Jiri Matas

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Auto-TLDR; Deep Depth-Aware Long-Term RGBD Tracking with Deep Discriminative Correlation Filter

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The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run. We reformulate deep discriminative correlation filter (DCF) to embed the depth information into deep features. Moreover, the same depth-aware correlation filter is used for target re- detection. Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.

VTT: Long-Term Visual Tracking with Transformers

Tianling Bian, Yang Hua, Tao Song, Zhengui Xue, Ruhui Ma, Neil Robertson, Haibing Guan

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Auto-TLDR; Visual Tracking Transformer with transformers for long-term visual tracking

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Long-term visual tracking is a challenging problem. State-of-the-art long-term trackers, e.g., GlobalTrack, utilize region proposal networks (RPNs) to generate target proposals. However, the performance of the trackers is affected by occlusions and large scale or ratio variations. To address these issues, in this paper, we are the first to propose a novel architecture with transformers for long-term visual tracking. Specifically, the proposed Visual Tracking Transformer (VTT) utilizes a transformer encoder-decoder architecture for aggregating global information to deal with occlusion and large scale or ratio variation. Furthermore, it also shows better discriminative power against instance-level distractors without the need for extra labeling and hard-sample mining. We conduct extensive experiments on three largest long-term tracking dataset and have achieved state-of-the-art performance.

One Step Clustering Based on A-Contrario Framework for Detection of Alterations in Historical Violins

Alireza Rezaei, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Piercarlo Dondi, Marco Malagodi

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Auto-TLDR; A-Contrario Clustering for the Detection of Altered Violins using UVIFL Images

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Preventive conservation is an important practice in Cultural Heritage. The constant monitoring of the state of conservation of an artwork helps us reduce the risk of damage and number of interventions necessary. In this work, we propose a probabilistic approach for the detection of alterations on the surface of historical violins based on an a-contrario framework. Our method is a one step NFA clustering solution which considers grey-level and spatial density information in one background model. The proposed method is robust to noise and avoids parameter tuning and any assumption about the quantity of the worn out areas. We have used as input UV induced fluorescence (UVIFL) images for considering details not perceivable with visible light. Tests were conducted on image sequences included in the ``Violins UVIFL imagery'' dataset. Results illustrate the ability of the algorithm to distinguish the worn area from the surrounding regions. Comparisons with the state of the art clustering methods shows improved overall precision and recall.

Visual Object Tracking in Drone Images with Deep Reinforcement Learning

Derya Gözen, Sedat Ozer

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Auto-TLDR; A Deep Reinforcement Learning based Single Object Tracker for Drone Applications

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There is an increasing demand on utilizing camera equipped drones and their applications in many domains varying from agriculture to entertainment and from sports events to surveillance. In such drone applications, an essential and a common task is tracking an object of interest visually. Drone (or UAV) images have different properties when compared to the ground taken (natural) images and those differences introduce additional complexities to the existing object trackers to be directly applied on drone applications. Some important differences among those complexities include (i) smaller object sizes to be tracked and (ii) different orientations and viewing angles yielding different texture and features to be observed. Therefore, new algorithms trained on drone images are needed for the drone-based applications. In this paper, we introduce a deep reinforcement learning (RL) based single object tracker that tracks an object of interest in drone images by estimating a series of actions to find the location of the object in the next frame. This is the first work introducing a single object tracker using a deep RL-based technique for drone images. Our proposed solution introduces a novel reward function that aims to reduce the total number of actions taken to estimate the object's location in the next frame and also introduces a different backbone network to be used on low resolution images. Additionally, we introduce a set of new actions into the action library to better deal with the above-mentioned complexities. We compare our proposed solutions to a state of the art tracking algorithm from the recent literature and demonstrate up to 3.87\% improvement in precision and 3.6\% improvement in IoU values on the VisDrone2019 dataset. We also provide additional results on OTB-100 dataset and show up to 3.15\% improvement in precision on the OTB-100 dataset when compared to the same previous state of the art algorithm. Lastly, we analyze the ability to handle some of the challenges faced during tracking, including but not limited to occlusion, deformation, and scale variation for our proposed solutions.

An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes

Mehdi Miah, Justine Pepin, Nicolas Saunier, Guillaume-Alexandre Bilodeau

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Auto-TLDR; Evaluating Appearance Features for Multiple Object Tracking in Urban Scenes

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This paper addresses the problem of selecting appearance features for multiple object tracking (MOT) in urban scenes. Over the years, a large number of features has been used for MOT. However, it is not clear whether some of them are better than others. Commonly used features are color histograms, histograms of oriented gradients, deep features from convolutional neural networks and re-identification (ReID) features. In this study, we assess how good these features are at discriminating objects enclosed by a bounding box in urban scene tracking scenarios. Several affinity measures, namely the L1, L2 and the Bhattacharyya distances, Rank-1 counts and the cosine similarity, are also assessed for their impact on the discriminative power of the features. Results on several datasets show that features from ReID networks are the best for discriminating instances from one another regardless of the quality of the detector. If a ReID model is not available, color histograms may be selected if the detector has a good recall and there are few occlusions; otherwise, deep features are more robust to detectors with lower recall.

A Novel Random Forest Dissimilarity Measure for Multi-View Learning

Hongliu Cao, Simon Bernard, Robert Sabourin, Laurent Heutte

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Auto-TLDR; Multi-view Learning with Random Forest Relation Measure and Instance Hardness

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Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-Identification

Toby Breckon, Aishah Alsehaim

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Auto-TLDR; ResNet50-IBN for Video-based Person Re-Identification using Single Stream 2D Convolution Network

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Video-based person re-identification has received increasing attention recently, as it plays an important role within the surveillance video analysis. Video-based Re-ID is an expansion of earlier image-based re-identification methods by learning features from a video via multiple image frames for each person. Most contemporary video Re-ID methods utilise complex CNN-based network architectures using 3D convolution or multi-branch networks to extract spatial-temporal features from the video. By contrast, in this paper, we will illustrate superior performance from a simple single stream 2D convolution network leveraging the ResNet50-IBN architecture to extract frame-level features followed by temporal attention for clip level features. These clip level features can be generalised to extract video level features by averaging clip level features without any additional cost. Our model, uses best video Re-ID practice and transfer learning between datasets, outperforms existing state-of-the-art approaches on MARS, PRID2011 and iLIDSVID datasets with 89:62%, 97:75%, 97:33% rank-1 accuracy respectively and with 84:61% mAP for MARS, without reliance on complex and memory intensive 3D convolutions or multistream networks architectures as found in other contemporary work. Conversely, this work shows that global features extracted by the 2D convolution network are a sufficient representation for robust state of the art video Re-ID.

RSINet: Rotation-Scale Invariant Network for Online Visual Tracking

Yang Fang, Geunsik Jo, Chang-Hee Lee

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Auto-TLDR; RSINet: Rotation-Scale Invariant Network for Adaptive Tracking

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Most Siamese network-based trackers perform the tracking process without model update, and cannot learn target-specific variation adaptively. Moreover, Siamese-based trackers infer the new state of tracked objects by generating axis-aligned bounding boxes, which contain extra background noise, and are unable to accurately estimate the rotation and scale transformation of moving objects, thus potentially reducing tracking performance. In this paper, we propose a novel Rotation-Scale Invariant Network (RSINet) to address the above problem. Our RSINet tracker consists of a target-distractor discrimination branch and a rotation-scale estimation branch, the rotation and scale knowledge can be explicitly learned by a multi-task learning method in an end-to-end manner. In addtion, the tracking model is adaptively optimized and updated under spatio-temporal energy control, which ensures model stability and reliability, as well as high tracking efficiency. Comprehensive experiments on OTB-100, VOT2018, and LaSOT benchmarks demonstrate that our proposed RSINet tracker yields new state-of-the-art performance compared with recent trackers, while running at real-time speed about 45 FPS.

Anime Sketch Colorization by Component-Based Matching Using Deep Appearance Features and Graph Representation

Thien Do, Pham Van, Anh Nguyen, Trung Dang, Quoc Nguyen, Bach Hoang, Giao Nguyen

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Auto-TLDR; Combining Deep Learning and Graph Representation for Sketch Colorization

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Sketch colorization is usually expensive and time-consuming for artists, and automating this process can have many pragmatic applications in the animation, comic book, and video game industry. However, automatic image colorization faces many challenges, because sketches not only lack texture information but also potentially entail complicated objects that require acute coloring. These difficulties usually result in incorrect color assignments that can ruin the aesthetic appeal of the final output. In this paper, we present a novel component-based matching framework that combines deep learned features and quadratic programming {\color{red} with a new cost function} to solve this colorization problem. The proposed framework inputs a character's sketches as well as a colored image in the same cut of a movie, and outputs a high-quality sequence of colorized frames based on the color assignment in the reference colored image. To carry out this colorization task, we first utilize a pretrained ResNet-34 model to extract elementary components' features to match certain pairs of components (one component from the sketch and one from reference). Next, a graph representation is constructed in order to process and match the remaining components that could not be done in the first step. Since the first step has reduced the number of components to be matched by the graph, we can solve this graph problem in a short computing time even when there are hundreds of different components present in each sketch. We demonstrate the effectiveness of the proposed solution by conducting comprehensive experiments and producing aesthetically pleasing results. To the best of our knowledge, our framework is the first work that combines deep learning and graph representation to colorize anime and achieves a high pixel-level accuracy at a reasonable time cost.

SSDL: Self-Supervised Domain Learning for Improved Face Recognition

Samadhi Poornima Kumarasinghe Wickrama Arachchilage, Ebroul Izquierdo

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Auto-TLDR; Self-supervised Domain Learning for Face Recognition in unconstrained environments

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Face recognition in unconstrained environments is challenging due to variations in illumination, quality of sensing, motion blur and etc. An individual’s face appearance can vary drastically under different conditions creating a gap between train (source) and varying test (target) data. The domain gap could cause decreased performance levels in direct knowledge transfer from source to target. Despite fine-tuning with domain specific data could be an effective solution, collecting and annotating data for all domains is extremely expensive. To this end, we propose a self-supervised domain learning (SSDL) scheme that trains on triplets mined from unlabelled data. A key factor in effective discriminative learning, is selecting informative triplets. Building on most confident predictions, we follow an “easy-to-hard” scheme of alternate triplet mining and self-learning. Comprehensive experiments on four different benchmarks show that SSDL generalizes well on different domains.

How to Define a Rejection Class Based on Model Learning?

Sarah Laroui, Xavier Descombes, Aurelia Vernay, Florent Villiers, Francois Villalba, Eric Debreuve

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Auto-TLDR; An innovative learning strategy for supervised classification that is able, by design, to reject a sample as not belonging to any of the known classes

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In supervised classification, the learning process typically trains a classifier to optimize the accuracy of classifying data into the classes that appear in the learning set, and only them. While this framework fits many use cases, there are situations where the learning process is knowingly performed using a learning set that only represents the data that have been observed so far among a virtually unconstrained variety of possible samples. It is then crucial to define a classifier which has the ability to reject a sample, i.e., to classify it into a rejection class that has not been yet defined. Although obvious solutions can add this ability a posteriori to a classifier that has been learned classically, a better approach seems to directly account for this requirement in the classifier design. In this paper, we propose an innovative learning strategy for supervised classification that is able, by design, to reject a sample as not belonging to any of the known classes. For that, we rely on modeling each class as the combination of a probability density function (PDF) and a threshold that is computed with respect to the other classes. Several alternatives are proposed and compared in this framework. A comparison with straightforward approaches is also provided.

ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection

Junting Fang, Xiaoyang Tan, Yuhui Wang

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Auto-TLDR; Attention Cascade R-CNN with Mix Non-Maximum Suppression for Robust Metal Defect Detection

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Metallic surface defect detection is of great significance in quality control for production. However, this task is very challenging due to the noise disturbance, large appearance variation, and the ambiguous definition of the defect individual. Traditional image processing methods are unable to detect the damaged region effectively and efficiently. In this paper, we propose a new defect detection method, Attention Cascade R-CNN with Mix-NMS (ACRM), to classify and locate defects robustly. Three submodules are developed to achieve this goal: 1) a lightweight attention block is introduced, which can improve the ability in capture global and local feature both in the spatial and channel dimension; 2) we firstly apply the cascade R-CNN to our task, which exploits multiple detectors to sequentially refine the detection result robustly; 3) we introduce a new method named Mix Non-Maximum Suppression (Mix-NMS), which can significantly improve its ability in filtering the redundant detection result in our task. Extensive experiments on a real industrial dataset show that ACRM achieves state-of-the-art results compared to the existing methods, demonstrating the effectiveness and robustness of our detection method.

Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos

Mamshad Nayeem Rizve, Ugur Demir, Praveen Praveen Tirupattur, Aayush Jung Rana, Kevin Duarte, Ishan Rajendrakumar Dave, Yogesh Rawat, Mubarak Shah

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Auto-TLDR; Gabriella: A Real-Time Online System for Activity Detection in Surveillance Videos

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Activity detection in surveillance videos is a difficult problem due to multiple factors such as large field of view, presence of multiple activities, varying scales and viewpoints, and its untrimmed nature. The existing research in activity detection is mainly focused on datasets, such as UCF-101, JHMDB, THUMOS, and AVA, which partially address these issues. The requirement of processing the surveillance videos in real-time makes this even more challenging. In this work we propose Gabriella, a real-time online system to perform activity detection on untrimmed surveillance videos. The proposed method consists of three stages: tubelet extraction, activity classification, and online tubelet merging. For tubelet extraction, we propose a localization network which takes a video clip as input and spatio-temporally detects potential foreground regions at multiple scales to generate action tubelets. We propose a novel Patch-Dice loss to handle large variations in actor size. Our online processing of videos at a clip level drastically reduces the computation time in detecting activities. The detected tubelets are assigned activity class scores by the classification network and merged together using our proposed Tubelet-Merge Action-Split (TMAS) algorithm to form the final action detections. The TMAS algorithm efficiently connects the tubelets in an online fashion to generate action detections which are robust against varying length activities. We perform our experiments on the VIRAT and MEVA (Multiview Extended Video with Activities) datasets and demonstrate the effectiveness of the proposed approach in terms of speed ($\sim$100 fps) and performance with state-of-the-art results. The code and models will be made publicly available.

An Adaptive Video-To-Video Face Identification System Based on Self-Training

Eric Lopez-Lopez, Carlos V. Regueiro, Xosé M. Pardo

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Auto-TLDR; Adaptive Video-to-Video Face Recognition using Dynamic Ensembles of SVM's

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Video-to-video face recognition in unconstrained conditions is still a very challenging problem, as the combination of several factors leads to an in general low-quality of facial frames. Besides, in some real contexts, the availability of labelled samples is limited, or data is streaming or it is only available temporarily due to storage constraints or privacy issues. In these cases, dealing with learning as an unsupervised incremental process is a feasible option. This work proposes a system based on dynamic ensembles of SVM's, which uses the ideas of self-training to perform adaptive Video-to-video face identification. The only label requirements of the system are a few frames (5 in our experiments) directly taken from the video-surveillance stream. The system will autonomously use additional video-frames to update and improve the initial model in an unsupervised way. Results show a significant improvement in comparison to other state-of-the-art static models.

Learning Defects in Old Movies from Manually Assisted Restoration

Arthur Renaudeau, Travis Seng, Axel Carlier, Jean-Denis Durou, Fabien Pierre, Francois Lauze, Jean-François Aujol

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Auto-TLDR; U-Net: Detecting Defects in Old Movies by Inpainting Techniques

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We propose to detect defects in old movies, as the first step of a larger framework of old movies restoration by inpainting techniques. The specificity of our work is to learn a film restorer's expertise from a pair of sequences, composed of a movie with defects, and the same movie which was semi-automatically restored with the help of a specialized software. In order to detect those defects with minimal human interaction and further reduce the time spent for a restoration, we feed a U-Net with consecutive defective frames as input to detect the unexpected variations of pixel intensity over space and time. Since the output of the network is a mask of defect location, we first have to create the dataset of mask frames on the basis of restored frames from the software used by the film restorer, instead of classical synthetic ground truth, which is not available. These masks are estimated by computing the absolute difference between restored frames and defectuous frames, combined with thresholding and morphological closing. Our network succeeds in automatically detecting real defects with more precision than the manual selection with an all-encompassing shape, including some the expert restorer could have missed for lack of time.

Detective: An Attentive Recurrent Model for Sparse Object Detection

Amine Kechaou, Manuel Martinez, Monica Haurilet, Rainer Stiefelhagen

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Auto-TLDR; Detective: An attentive object detector that identifies objects in images in a sequential manner

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In this work, we present Detective – an attentive object detector that identifies objects in images in a sequential manner. Our network is based on an encoder-decoder architecture, where the encoder is a convolutional neural network, and the decoder is a convolutional recurrent neural network coupled with an attention mechanism. At each iteration, our decoder focuses on the relevant parts of the image using an attention mechanism, and then estimates the object’s class and the bounding box coordinates. Current object detection models generate dense predictions and rely on post-processing to remove duplicate predictions. Detective is a sparse object detector that generates a single bounding box per object instance. However, training a sparse object detector is challenging, as it requires the model to reason at the instance level and not just at the class and spatial levels. We propose a training mechanism based on the Hungarian Algorithm and a loss that balances the localization and classification tasks. This allows Detective to achieve promising results on the PASCAL VOC object detection dataset. Our experiments demonstrate that sparse object detection is possible and has a great potential for future developments in applications where the order of the objects to be predicted is of interest.

A New Geodesic-Based Feature for Characterization of 3D Shapes: Application to Soft Tissue Organ Temporal Deformations

Karim Makki, Amine Bohi, Augustin Ogier, Marc-Emmanuel Bellemare

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Auto-TLDR; Spatio-Temporal Feature Descriptors for 3D Shape Characterization from Point Clouds

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Spatio-temporal feature descriptors are of great importance for characterizing the local changes of 3D deformable shapes. In this study, we propose a method for characterizing 3D shapes from point clouds and we show a direct application on a study of organ temporal deformations. As an example, we characterize the behavior of the bladder during forced respiratory motion with a reduced number of 3D surface points: first, a set of equidistant points representing the vertices of quadrilateral mesh for the organ surface are tracked throughout a long dynamic MRI sequence using a large deformation diffeomorphic metric mapping (LDDMM) framework. Second, a novel 3D shape descriptor invariant to translation, scale and rotation is proposed for characterizing the temporal organ deformations by employing an Eulerian Partial Differential Equations (PDEs) methodology. We demonstrate the robustness of our feature on both synthetic 3D shapes and realistic dynamic Magnetic Resonance Imaging (MRI) data sequences portraying the bladder deformation during a forced breathing exercise. Promising results are obtained, showing that the proposed feature may be useful for several computer vision applications such as medical imaging, aerodynamics and robotics.

Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Olivier Moliner, Sangxia Huang, Kalle Åström

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Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

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Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

Tackling Occlusion in Siamese Tracking with Structured Dropouts

Deepak Gupta, Efstratios Gavves, Arnold Smeulders

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Auto-TLDR; Structured Dropout for Occlusion in latent space

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Occlusion is one of the most difficult challenges in object tracking to model. This is because unlike other challenges, where data augmentation can be of help, occlusion is hard to simulate as the occluding object can be anything in any shape. In this paper, we propose a simple solution to simulate the effects of occlusion in the latent space. Specifically, we present structured dropout to mimic the change in latent codes under occlusion. We present three forms of dropout (channel dropout, segment dropout and slice dropout) with the various forms of occlusion in mind. To demonstrate its effectiveness, the dropouts are incorporated into two modern Siamese trackers (SiamFC and SiamRPN++). The outputs from multiple dropouts are combined using an encoder network to obtain the final prediction. Experiments on several tracking benchmarks show the benefits of structured dropouts, while due to their simplicity requiring only small changes to the existing tracker models.