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Unsupervised Moving Object Detection through Background Models for PTZ Camera

Kimin Yun, Hyung-Il Kim, Kangmin Bae, Jongyoul Park

Auto-TLDR; Unsupervised Moving Object Detection in a PTZ Camera through Two Background Models

Abstract Slides Poster

Moving object detection in a video plays an important role in many vision applications. Recently, moving object detection using appearance modeling based on a convolutional neural network has been actively developed. However, the CNN-based methods usually require the user's supervision of the first frame so that it becomes highly dependent on the training dataset. In contrast, the method of finding a foreground, which models a background occupying a large proportion in an image, can detect a moving object efficiently in an unsupervised manner. However, existing methods based on background modeling in a pan-tilt-zoom (PTZ) camera suffer many false positives or loss of moving objects due to the estimation error of camera motion. To overcome the aforementioned limitations, we propose a moving object detection method for a PTZ camera through two background models. In an unsupervised way, our method builds the two background models that have different roles: 1) a coarse background model for detecting large changes, and 2) a fine background model for detecting small changes. In more detail, the coarse background model builds a block-based Gaussian model, and the fine model builds a sample consensus model. Both models are adaptively updated according to the estimated camera motion in the video recorded by a PTZ camera. Then, each foreground result from two background models is incorporated to fill the moving object region. Through experiments, the proposed method achieves better performance than the state-of-the-art methods and operates in real-time without parallel processing. In addition, we showed the effectiveness of the proposed model through improved results of moving object detection through combination with the latest supervised method.

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Coarse-To-Fine Foreground Segmentation Based on Co-Occurrence Pixel-Block and Spatio-Temporal Attention Model

Xinyu Liu, Dong Liang

Auto-TLDR; Foreground Segmentation from coarse to Fine Using Co-occurrence Pixel-Block Model for Dynamic Scene

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Foreground segmentation in dynamic scene is an important task in video surveillance. The unsupervised background subtraction method based on background statistics modeling has difficulties in updating. On the other hand, the supervised foreground segmentation method based on deep learning relies on the large-scale of accurately annotated training data, which limits its cross-scene performance. In this paper, we propose a foreground segmentation method from coarse to fine. First, a across-scenes trained Spatio-Temporal Attention Model (STAM) is used to achieve coarse segmentation, which does not require training on specific scene. Then the coarse segmentation is used as a reference to help Co-occurrence Pixel-Block Model (CPB) complete the fine segmentation, and at the same time help CPB to update its background model. This method is more flexible than those deep-learning-based methods which depends on the specific-scene training, and realizes the accurate online dynamic update of the background model. Experimental results on WallFlower and LIMU validate our method outperforms STAM, CPB and other methods of participating in comparison.

Motion U-Net: Multi-Cue Encoder-Decoder Network for Motion Segmentation

Gani Rahmon, Filiz Bunyak, Kannappan Palaniappan

Auto-TLDR; Motion U-Net: A Deep Learning Framework for Robust Moving Object Detection under Challenging Conditions

Abstract Slides Poster Similar

Detection of moving objects is a critical first step in many computer vision applications. Several algorithms for motion and change detection were proposed. However, many of these approaches lack the ability to handle challenging real-world scenarios. Recently, deep learning approaches started to produce impressive solutions to computer vision tasks, particularly for detection and segmentation. Many existing deep learning networks proposed for moving object detection rely only on spatial appearance cues. In this paper, we propose a novel multi-cue and multi-stream network, Motion U-Net (MU-Net), which integrates motion, change, and appearance cues using a deep learning framework for robust moving object detection under challenging conditions. The proposed network consists of a two-stream encoder module followed by feature concatenation and a decoder module. Motion and change cues are computed through our tensor-based motion estimation and a multi-modal background subtraction modules. The proposed system was tested and evaluated on the change detection challenge datasets (CDnet-2014) and compared to state-of-the-art methods. On CDnet-2014 dataset, our approach reaches an average overall F-measure of 0.9852 and outperforms all current state-of-the-art methods. The network was also tested on the unseen SBI-2015 dataset and produced promising results.

Accurate Background Subtraction Using Dynamic Object Presence Probability in Sports Scenes

Ryosuke Watanabe, Jun Chen, Tomoaki Konno, Sei Naito

Auto-TLDR; DOPP: Dynamic Object Presence Probabilistic Background Subtraction for Foreground Segmentation

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Foreground segmentation technologies play an important role in applications such as free-viewpoint video (FVV) and sports video analysis. In this situation, we propose a new method that achieves accurate foreground silhouette extraction using dynamic object presence probability (DOPP). Our main contributions are as follows. 1) Object presence probability for each pixel is calculated from the object recognition results based on deep learning. After that, background subtraction is implemented by changing the threshold and the update rate of the background model in response to the object presence probability. Parameter tuning of background subtraction is executed by using the object recognition results to improve the silhouette extraction quality. 2) To calculate more accurate silhouette images, parameters of background subtraction are adjusted by monitoring optical flows between consecutive frames. The object presence probability of the current frame is dynamically updated by using the object presence probability of the previous frame with optical flows. In the experiments, we confirmed that the proposed method achieved more accurate silhouette extraction than conventional methods in three sports sequences.

GraphBGS: Background Subtraction Via Recovery of Graph Signals

Jhony Heriberto Giraldo Zuluaga, Thierry Bouwmans

Auto-TLDR; Graph BackGround Subtraction using Graph Signals

Abstract Slides Poster Similar

Background subtraction is a fundamental pre-processing task in computer vision. This task becomes challenging in real scenarios due to variations in the background for both static and moving camera sequences. Several deep learning methods for background subtraction have been proposed in the literature with competitive performances. However, these models show performance degradation when tested on unseen videos; and they require huge amount of data to avoid overfitting. Recently, graph-based algorithms have been successful approaching unsupervised and semi-supervised learning problems. Furthermore, the theory of graph signal processing and semi-supervised learning have been combined leading to new insights in the field of machine learning. In this paper, concepts of recovery of graph signals are introduced in the problem of background subtraction. We propose a new algorithm called Graph BackGround Subtraction (GraphBGS), which is composed of: instance segmentation, background initialization, graph construction, graph sampling, and a semi-supervised algorithm inspired from the theory of recovery of graph signals. Our algorithm has the advantage of requiring less data than deep learning methods while having competitive results on both: static and moving camera videos. GraphBGS outperforms unsupervised and supervised methods in several challenging conditions on the publicly available Change Detection (CDNet2014), and UCSD background subtraction databases.

Early Wildfire Smoke Detection in Videos

Taanya Gupta, Hengyue Liu, Bir Bhanu

Auto-TLDR; Semi-supervised Spatio-Temporal Video Object Segmentation for Automatic Detection of Smoke in Videos during Forest Fire

Abstract Poster Similar

Recent advances in unmanned aerial vehicles and camera technology have proven useful for the detection of smoke that emerges above the trees during a forest fire. Automatic detection of smoke in videos is of great interest to Fire department. To date, in most parts of the world, the fire is not detected in its early stage and generally it turns catastrophic. This paper introduces a novel technique that integrates spatial and temporal features in a deep learning framework using semi-supervised spatio-temporal video object segmentation and dense optical flow. However, detecting this smoke in the presence of haze and without the labeled data is difficult. Considering the visibility of haze in the sky, a dark channel pre-processing method is used that reduces the amount of haze in video frames and consequently improves the detection results. Online training is performed on a video at the time of testing that reduces the need for ground-truth data. Tests using the publicly available video datasets show that the proposed algorithms outperform previous work and they are robust across different wildfire-threatened locations.

Learning Object Deformation and Motion Adaption for Semi-Supervised Video Object Segmentation

Xiaoyang Zheng, Xin Tan, Jianming Guo, Lizhuang Ma

Auto-TLDR; Semi-supervised Video Object Segmentation with Mask-propagation-based Model

Abstract Slides Poster Similar

We propose a novel method to solve the task of semi-supervised video object segmentation in this paper, where the mask annotation is only given at the first frame of the video sequence. A mask-propagation-based model is applied to learn the past and current information for segmentation. Besides, due to the scarcity of training data, image/mask pairs that model object deformation and shape variance are generated for the training phase. In addition, we generate the key flips between two adjacent frames for motion adaptation. The method works in an end-to-end way, without any online fine-tuning on test videos. Extensive experiments demonstrate that our method achieves competitive performance against state-of-the-art algorithms on benchmark datasets, covering cases with single object or multiple objects. We also conduct extensive ablation experiments to analyze the effectiveness of our proposed method.

Tracking Fast Moving Objects by Segmentation Network

Ales Zita, Filip Sroubek

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.

Reducing False Positives in Object Tracking with Siamese Network

Takuya Ogawa, Takashi Shibata, Shoji Yachida, Toshinori Hosoi

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.

Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation

Dexiang Hong, Guorong Li, Kai Xu, Li Su, Qingming Huang

Auto-TLDR; Siamese Dynamic Mask Estimation for Video Object Segmentation

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Video object segmentation(VOS) has been a fundamental topic in recent years, and many deep learning-based methods have achieved state-of-the-art performance on multiple benchmarks. However, most of these methods rely on pixel-level matching between the template and the searched frames on the whole image while the targets only occupy a small region. Calculating on the entire image brings lots of additional computation cost. Besides, the whole image may contain some distracting information resulting in many false-positive matching points. To address this issue, motivated by one-stage instance object segmentation methods, we propose an efficient siamese dynamic mask estimation network for fast video object segmentation. The VOS is decoupled into two tasks, i.e. mask feature learning and dynamic kernel prediction. The former is responsible for learning high-quality features to preserve structural geometric information, and the latter learns a dynamic kernel which is used to convolve with the mask feature to generate a mask output. We use Siamese neural network as a feature extractor and directly predict masks after correlation. In this way, we can avoid using pixel-level matching, making our framework more simple and efficient. Experiment results on DAVIS 2016 /2017 datasets show that our proposed methods can run at 35 frames per second on NVIDIA RTX TITAN while preserving competitive accuracy.

Revisiting Sequence-To-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory

Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio, Federico Raue, Jörn Hees, Andreas Dengel

Auto-TLDR; Sequence-to-Sequence Learning for Video Object Segmentation

Abstract Slides Poster Similar

Video Object Segmentation (VOS) is an active research area of the visual domain. One of its fundamental sub-tasks is semi-supervised / one-shot learning: given only the segmentation mask for the first frame, the task is to provide pixel-accurate masks for the object over the rest of the sequence. Despite much progress in the last years, we noticed that many of the existing approaches lose objects in longer sequences, especially when the object is small or briefly occluded. In this work, we build upon a sequence-to-sequence approach that employs an encoder-decoder architecture together with a memory module for exploiting the sequential data. We further improve this approach by proposing a model that manipulates multi-scale spatio-temporal information using memory-equipped skip connections. Furthermore, we incorporate an auxiliary task based on distance classification which greatly enhances the quality of edges in segmentation masks. We compare our approach to the state of the art and show considerable improvement in the contour accuracy metric and the overall segmentation accuracy.

ACCLVOS: Atrous Convolution with Spatial-Temporal ConvLSTM for Video Object Segmentation

Muzhou Xu, Shan Zong, Chunping Liu, Shengrong Gong, Zhaohui Wang, Yu Xia

Auto-TLDR; Semi-supervised Video Object Segmentation using U-shape Convolution and ConvLSTM

Abstract Slides Poster Similar

Semi-supervised video object segmentation aims at segmenting the target of interest throughout a video sequence when only the annotated mask of the first frame is given. A feasible method for segmentation is to capture the spatial-temporal coherence between frames. However, it may suffer from mask drift when the spatial-temporal coherence is unreliable. To relieve this problem, we propose an encoder-decoder-recurrent model for semi-supervised video object segmentation. The model adopts a U-shape architecture that combines atrous convolution and ConvLSTM to establish the coherence in both the spatial and temporal domains. Furthermore, the weight ratio for each block is also reconstructed to make the model more suitable for the VOS task. We evaluate our method on two benchmarks, DAVIS-2017 and Youtube-VOS, where state-of-the-art segmentation accuracy with a real-time inference speed of 21.3 frames per second on a Tesla P100 is obtained.

Visual Saliency Oriented Vehicle Scale Estimation

Qixin Chen, Tie Liu, Jiali Ding, Zejian Yuan, Yuanyuan Shang

Auto-TLDR; Regularized Intensity Matching for Vehicle Scale Estimation with salient object detection

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Vehicle scale estimation with a single camera is a typical application for intelligent transportation and it faces the challenges from visual computing while intensity-based method and descriptor-based method should be balanced. This paper proposed a vehicle scale estimation method based on salient object detection to resolve this problem. The regularized intensity matching method is proposed in Lie Algebra to achieve robust and accurate scale estimation, and descriptor matching and intensity matching are combined to minimize the proposed loss function. The visual attention mechanism is designed to select image patches with texture and remove the occluded image patches. Then the weights are assigned to pixels from the selected image patches which alleviates the influence of noise-corrupted pixels. The experiments show that the proposed method significantly outperforms state-of-the-art methods with regard to the robustness and accuracy of vehicle scale estimation.

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

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.

Ground-truthing Large Human Behavior Monitoring Datasets

Tehreem Qasim, Robert Fisher, Naeem Bhatti

Auto-TLDR; Semi-automated Groundtruthing for Large Video Datasets

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We present a groundtruthing approach which is applicable to large video datasets collected for studying people’s behavior, and which are recorded at a low frame per second (fps) rate. Groundtruthing a large dataset manually is a time consuming task and is prone to errors. The proposed approach is semi-automated (using a combination of deepnet and traditional image analysis) to minimize human labeler’s interaction with the video frames. The framework employs mask-rcnn as a people counter followed by human assisted semi-automated tests to correct the wrong labels. Subsequently, a bounding box extraction algorithm is used which is fully automated for frames with a single person and semi-automated for frames with two or more people. We also propose a methodology for anomaly detection i.e., collapse on table or floor. Behavior recognition is performed by using a fine-tuned alexnet convolutional neural network. The people detection and behavior analysis components of the framework are primarily designed to help reduce human labor in ground-truthing so that minimal human involvement is required. They are not meant to be employed as fully automated state-of-the-art systems. The proposed approach is validated on a new dataset presented in this paper, containing human activity in an indoor office environment and recorded at 1 fps as well as an indoor video sequence recorded at 15 fps. Experimental results show a significant reduction in human labor involved in the process of ground-truthing i.e., the number of potential clicks for office dataset was reduced by 99.2% and for the additional test video by 99.7%.

Human Segmentation with Dynamic LiDAR Data

Tao Zhong, Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi

Auto-TLDR; Spatiotemporal Neural Network for Human Segmentation with Dynamic Point Clouds

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Consecutive LiDAR scans and depth images compose dynamic 3D sequences, which contain more abundant spatiotemporal information than a single frame. Similar to the development history of image and video perception, dynamic 3D sequence perception starts to come into sight after inspiring research on static 3D data perception. This work proposes a spatiotemporal neural network for human segmentation with the dynamic LiDAR point clouds. It takes a sequence of depth images as input. It has a two-branch structure, i.e., the spatial segmentation branch and the temporal velocity estimation branch. The velocity estimation branch is designed to capture motion cues from the input sequence and then propagates them to the other branch. So that the segmentation branch segments humans according to both spatial and temporal features. These two branches are jointly learned on a generated dynamic point cloud data set for human recognition. Our works fill in the blank of dynamic point cloud perception with the spherical representation of point cloud and achieves high accuracy. The experiments indicate that the introduction of temporal feature benefits the segmentation of dynamic point cloud perception.

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

Chengliang Wang, Xin Xie, Chao Liao

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.

Object Segmentation Tracking from Generic Video Cues

Amirhossein Kardoost, Sabine Müller, Joachim Weickert, Margret Keuper

Auto-TLDR; A Light-Weight Variational Framework for Video Object Segmentation in Videos

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We propose a light-weight variational framework for online tracking of object segmentations in videos based on optical flow and image boundaries. While high-end computer vision methods on this task rely on sequence specific training of dedicated CNN architectures, we show the potential of a variational model, based on generic video information from motion and color. Such cues are usually required for tasks such as robot navigation or grasp estimation. We leverage them directly for video object segmentation and thus provide accurate segmentations at potentially very low extra cost. Our simple method can provide competitive results compared to the costly CNN-based methods with parameter tuning. Furthermore, we show that our approach can be combined with state-of-the-art CNN-based segmentations in order to improve over their respective results. We evaluate our method on the datasets DAVIS 16,17 and SegTrack v2.

Video Semantic Segmentation Using Deep Multi-View Representation Learning

Akrem Sellami, Salvatore Tabbone

Auto-TLDR; Deep Multi-view Representation Learning for Video Object Segmentation

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In this paper, we propose a deep learning model based on deep multi-view representation learning, to address the video object segmentation task. The proposed model emphasizes the importance of the inherent correlation between video frames and incorporates a multi-view representation learning based on deep canonically correlated autoencoders. The multi-view representation learning in our model provides an efficient mechanism for capturing inherent correlations by jointly extracting useful features and learning better representation into a joint feature space, i.e., shared representation. To increase the training data and the learning capacity, we train the proposed model with pairs of video frames, i.e., $F_{a}$ and $F_{b}$. During the segmentation phase, the deep canonically correlated autoencoders model encodes useful features by processing multiple reference frames together, which is used to detect the frequently reappearing. Our model enhances the state-of-the-art deep learning-based methods that mainly focus on learning discriminative foreground representations over appearance and motion. Experimental results over two large benchmarks demonstrate the ability of the proposed method to outperform competitive approaches and to reach good performances, in terms of semantic segmentation.

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

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.

Dynamic Resource-Aware Corner Detection for Bio-Inspired Vision Sensors

Sherif Abdelmonem Sayed Mohamed, Jawad Yasin, Mohammad-Hashem Haghbayan, Antonio Miele, Jukka Veikko Heikkonen, Hannu Tenhunen, Juha Plosila

Auto-TLDR; Three Layer Filtering-Harris Algorithm for Event-based Cameras in Real-Time

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Event-based cameras are vision devices that transmit only brightness changes with low latency and ultra-low power consumption. Such characteristics make event-based cameras attractive in the field of localization and object tracking in resource-constrained systems. Since the number of generated events in such cameras is huge, the selection and filtering of the incoming events are beneficial from both increasing the accuracy of the features and reducing the computational load. In this paper, we present an algorithm to detect asynchronous corners form a stream of events in real-time on embedded systems. The algorithm is called the Three Layer Filtering-Harris or TLF-Harris algorithm. The algorithm is based on an events' filtering strategy whose purpose is 1) to increase the accuracy by deliberately eliminating some incoming events, i.e., noise and 2) to improve the real-time performance of the system, i.e., preserving a constant throughput in terms of input events per second, by discarding unnecessary events with a limited accuracy loss. An approximation of the Harris algorithm, in turn, is used to exploit its high-quality detection capability with a low-complexity implementation to enable seamless real-time performance on embedded computing platforms. The proposed algorithm is capable of selecting the best corner candidate among neighbors and achieves an average execution time savings of 59 % compared with the conventional Harris score. Moreover, our approach outperforms the competing methods, such as eFAST, eHarris, and FA-Harris, in terms of real-time performance, and surpasses Arc* in terms of accuracy.

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

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

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

Abstract Slides Poster Similar

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.

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

Mygel Andrei Martija, Prospero Naval

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.

User-Independent Gaze Estimation by Extracting Pupil Parameter and Its Mapping to the Gaze Angle

Sang Yoon Han, Nam Ik Cho

Auto-TLDR; Gaze Point Estimation using Pupil Shape for Generalization

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Since gaze estimation plays a crucial role in recognizing human intentions, it has been researched for a long time, and its accuracy is ever increasing. However, due to the wide variation in eye shapes and focusing abilities between the individuals, accuracies of most algorithms vary depending on each person in the test group, especially when the initial calibration is not well performed. To alleviate the user-dependency, we attempt to derive features that are general for most people and use them as the input to a deep network instead of using the images as the input. Specifically, we use the pupil shape as the core feature because it is directly related to the 3D eyeball rotation, and thus the gaze direction. While existing deep learning methods learn the gaze point by extracting various features from the image, we focus on the mapping function from the eyeball rotation to the gaze point by using the pupil shape as the input. It is shown that the accuracy of gaze point estimation also becomes robust for the uncalibrated points by following the characteristics of the mapping function. Also, our gaze network learns the gaze difference to facilitate the re-calibration process to fix the calibration-drift problem that typically occurs with glass-type or head-mount devices.

SiamMT: Real-Time Arbitrary Multi-Object Tracking

Lorenzo Vaquero, Manuel Mucientes, Victor Brea

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.

Temporal Feature Enhancement Network with External Memory for Object Detection in Surveillance Video

Masato Fujitake, Akihiro Sugimoto

Auto-TLDR; Temporal Attention Based External Memory Network for Surveillance Object Detection

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Video object detection is challenging and essential in practical applications, such as surveillance cameras for traffic control and public security. Unlike the video in natural scenes, the surveillance video tends to contain dense, and small objects (typically vehicles) in their appearances. Therefore, existing methods for surveillance object detection utilize still-image object detection approaches with rich feature extractors at the expense of their run-time speeds. The run-time speed, however, becomes essential when the video is being streamed. In this paper, we exploit temporal information in videos to enrich the feature maps, proposing the first temporal attention based external memory network for the live stream of video. Extensive experiments on real-world traffic surveillance benchmarks demonstrate the real-time performance of the proposed model while keeping comparable accuracy with state-of-the-art.

Mobile Augmented Reality: Fast, Precise, and Smooth Planar Object Tracking

Dmitrii Matveichev, Daw-Tung Lin

Auto-TLDR; Planar Object Tracking with Sparse Optical Flow Tracking and Descriptor Matching

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We propose an innovative method for combining sparse optical flow tracking and descriptor matching algorithms. The proposed approach solves the following problems that are inherent to keypoint-based and optical flow based tracking algorithms: spatial jitter, extreme scale transformation, extreme perspective transformation, degradation in the number of tracking points, and drifting of tracking points. Our algorithm provides smooth object-position tracking under six degrees of freedom transformations with a small computational cost for providing a high-quality real-time AR experience on mobile platforms. We experimentally demonstrate that our approach outperforms the state-of-the-art tracking algorithms while offering faster computational time. A mobile augmented reality (AR) application, which is developed using our approach, delivers planar object tracking with 30 FPS on modern mobile phones for a camera resolution of 1280$\times$720. Finally, we compare the performance of our AR application with that of the Vuforia-based AR application on the same planar objects database. The test results show that our AR application delivers better AR experience than Vuforia in terms of smooth transition of object-pose between video frames.

Learning to Segment Clustered Amoeboid Cells from Brightfield Microscopy Via Multi-Task Learning with Adaptive Weight Selection

Rituparna Sarkar, Suvadip Mukherjee, Elisabeth Labruyere, Jean-Christophe Olivo-Marin

Auto-TLDR; Supervised Cell Segmentation from Microscopy Images using Multi-task Learning in a Multi-Task Learning Paradigm

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Detecting and segmenting individual cells from microscopy images is critical to various life science applications. Traditional cell segmentation tools are often ill-suited for applications in brightfield microscopy due to poor contrast and intensity heterogeneity, and only a small subset are applicable to segment cells in a cluster. In this regard, we introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm. A combination of a multi-task loss, based on the region and cell boundary detection, is employed for an improved prediction efficiency of the network. The learning problem is posed in a novel min-max framework which enables adaptive estimation of the hyper-parameters in an automatic fashion. The region and cell boundary predictions are combined via morphological operations and active contour model to segment individual cells. The proposed methodology is particularly suited to segment touching cells from brightfield microscopy images without manual interventions. Quantitatively, we observe an overall Dice score of 0.93 on the validation set, which is an improvement of over 15.9% on a recent unsupervised method, and outperforms the popular supervised U-net algorithm by at least 5.8% on average.

RSINet: Rotation-Scale Invariant Network for Online Visual Tracking

Yang Fang, Geunsik Jo, Chang-Hee Lee

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.

Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model

Congcong Li, Haoyu Ma, Qingmin Liao

Auto-TLDR; Adaptive object scene flow estimation using a hybrid CNN-CRF model and adaptive iteration

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Scene flow estimation based on stereo sequences is a comprehensive task relevant to disparity and optical flow. Some existing methods are time-consuming and often fail in the presence of reflective surfaces. In this paper, we propose a two-stage adaptive object scene flow estimation method using a hybrid CNN-CRF model (ACOSF), which benefits from high-quality features and the structured modelling capability. Meanwhile, in order to balance the computational efficiency and accuracy, we employ adaptive iteration for energy function optimization, which is flexible and efficient for various scenes. Besides, we utilize high-quality pixel selection to reduce the computation time with only a slight decrease in accuracy. Our method achieves competitive results with the state-of-the-art, which ranks second on the challenging KITTI 2015 scene flow benchmark.

Learning to Segment Dynamic Objects Using SLAM Outliers

Dupont Romain, Mohamed Tamaazousti, Hervé Le Borgne

Auto-TLDR; Automatic Segmentation of Dynamic Objects Using SLAM Outliers Using Consensus Inversion

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We present a method to automatically learn to segment dynamic objects using SLAM outliers. It requires only one monocular sequence per dynamic object for training and consists in localizing dynamic objects using SLAM outliers, creating their masks, and using these masks to train a semantic segmentation network. We integrate the trained network in ORB-SLAM 2 and LDSO. At runtime we remove features on dynamic objects, making the SLAM unaffected by them. We also propose a new stereo dataset and new metrics to evaluate SLAM robustness. Our dataset includes consensus inversions, i.e., situations where the SLAM uses more features on dynamic objects that on the static background. Consensus inversions are challenging for SLAM as they may cause major SLAM failures. Our approach performs better than the State-of-the-Art on the TUM RGB-D dataset in monocular mode and on our dataset in both monocular and stereo modes.

RONELD: Robust Neural Network Output Enhancement for Active Lane Detection

Zhe Ming Chng, Joseph Mun Hung Lew, Jimmy Addison Lee

Auto-TLDR; Real-Time Robust Neural Network Output Enhancement for Active Lane Detection

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Accurate lane detection is critical for navigation in autonomous vehicles, particularly the active lane which demarcates the single road space that the vehicle is currently traveling on. Recent state-of-the-art lane detection algorithms utilize convolutional neural networks (CNNs) to train deep learning models on popular benchmarks such as TuSimple and CULane. While each of these models works particularly well on train and test inputs obtained from the same dataset, the performance drops significantly on unseen datasets of different environments. In this paper, we present a real-time robust neural network output enhancement for active lane detection (RONELD) method to identify, track, and optimize active lanes from deep learning probability map outputs. We first adaptively extract lane points from the probability map outputs, followed by detecting curved and straight lanes before using weighted least squares linear regression on straight lanes to fix broken lane edges resulting from fragmentation of edge maps in real images. Lastly, we hypothesize true active lanes through tracking preceding frames. Experimental results demonstrate an up to two-fold increase in accuracy using RONELD on cross-dataset validation tests.

Approach for Document Detection by Contours and Contrasts

Daniil Tropin, Sergey Ilyuhin, Dmitry Nikolaev, Vladimir V. Arlazarov

Auto-TLDR; A countor-based method for arbitrary document detection on a mobile device

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This paper considers the task of arbitrary document detection performed on a mobile device. The classical contour-based approach often mishandles cases with occlusion, complex background, or blur. Region-based approach, which relies on the contrast between object and background, does not have limitations, however its known implementations are highly resource-consuming. We propose a modification of a countor-based method, in which the competing hypotheses of the contour location are ranked according to the contrast between the areas inside and outside the border. In the performed experiments such modification leads to the 40% decrease of alternatives ordering errors and 10% decrease of the overall number of detection errors. We updated state-of-the-art performance on the open MIDV-500 dataset and demonstrated competitive results with the state-of-the-art on the SmartDoc dataset.

Motion-Supervised Co-Part Segmentation

Aliaksandr Siarohin, Subhankar Roy, Stéphane Lathuiliere, Sergey Tulyakov, Elisa Ricci, Nicu Sebe

Auto-TLDR; Self-supervised Co-Part Segmentation Using Motion Information from Videos

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Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation. Differently from previous works, our approach develops the idea that motion information inferred from videos can be leveraged to discover meaningful object parts. To this end, our method relies on pairs of frames sampled from the same video. The network learns to predict part segments together with a representation of the motion between two frames, which permits reconstruction of the target image. Through extensive experimental evaluation on publicly available video sequences we demonstrate that our approach can produce improved segmentation maps with respect to previous self-supervised co-part segmentation approaches.

Unconstrained Vision Guided UAV Based Safe Helicopter Landing

Arindam Sikdar, Abhimanyu Sahu, Debajit Sen, Rohit Mahajan, Ananda Chowdhury

Auto-TLDR; Autonomous Helicopter Landing in Hazardous Environments from Unmanned Aerial Images Using Constrained Graph Clustering

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In this paper, we have addressed the problem of automated detection of safe zone(s) for helicopter landing in hazardous environments from images captured by an Unmanned Aerial Vehicle (UAV). The unconstrained motion of the image capturing drone (the UAV in our case) makes the problem further difficult. The solution pipeline consists of natural landmark detection and tracking, stereo-pair generation using constrained graph clustering, digital terrain map construction and safe landing zone detection. The main methodological contribution lies in mathematically formulating epipolar constraint and then using it in a Minimum Spanning Tree (MST) based graph clustering approach. We have also made publicly available AHL (Autonomous Helicopter Landing) dataset, a new aerial video dataset captured by a drone, with annotated ground-truths. Experimental comparisons with other competing clustering methods i) in terms of Dunn Index and Davies Bouldin Index as well as ii) for frame-level safe zone detection in terms of F-measure and confusion matrix clearly demonstrate the effectiveness of the proposed formulation.

Multi-Camera Sports Players 3D Localization with Identification Reasoning

Yukun Yang, Ruiheng Zhang, Wanneng Wu, Yu Peng, Xu Min

Auto-TLDR; Probabilistic and Identified Occupancy Map for Sports Players 3D Localization

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Multi-camera sports players 3D localization is always a challenging task due to heavy occlusions in crowded sports scene. Traditional methods can only provide players locations without identification information. Existing methods of localization may cause ambiguous detection and unsatisfactory precision and recall, especially when heavy occlusions occur. To solve this problem, we propose a generic localization method by providing distinguishable results that have the probabilities of locations being occupied by players with unique ID labels. We design the algorithms with a multi-dimensional Bayesian model to create a Probabilistic and Identified Occupancy Map (PIOM). By using this model, we jointly apply deep learning-based object segmentation and identification to obtain sports players probable positions and their likely identification labels. This approach not only provides players 3D locations but also gives their ID information that are distinguishable from others. Experimental results demonstrate that our method outperforms the previous localization approaches with reliable and distinguishable outcomes.

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

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.

Semantic Segmentation for Pedestrian Detection from Motion in Temporal Domain

Guo Cheng, Jiang Yu Zheng

Auto-TLDR; Motion Proﬁle: Recognizing Pedestrians along with their Motion Directions in a Temporal Way

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In autonomous driving, state-of-the-art methods detect pedestrian through appearance in 2-D spatial images. However, these approaches are typically time-consuming because of the complexity of algorithms to cope with large variations in shape, pose, action, and illumination. They also fall short of capturing temporal continuity in motion trace. In a completely diﬀerent approach, this work recognizes pedestrians along with their motion directions in a temporal way. By projecting a driving video to a 2-D temporal image called Motion Proﬁle (MP), we can robustly distinguish pedestrian in motion and standing-still against smooth background motion. To ensure non-redundant data processing of deep network on a compact motion profile further, a novel temporal-shift memory (TSM) model is developed to perform deep learning of sequential input in linear processing time. In experiments containing various pedestrian motion from sensors such as video and LiDAR, we demonstrate that, with the data size around 3/720th of video volume, this motion-based method can reach the detecting rate of pedestrians at 90% in near and mid-range on the road. With a super-fast processing speed and good accuracy, this method is promising for intelligent vehicles.

Coarse to Fine: Progressive and Multi-Task Learning for Salient Object Detection

Dong-Goo Kang, Sangwoo Park, Joonki Paik

Auto-TLDR; Progressive and mutl-task learning scheme for salient object detection

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Most deep learning-based salient object detection (SOD) methods tried to manipulate the convolution block to effectively capture the context of object. In this paper, we propose a novel method, called progressive and mutl-task learning scheme, to extract the context of object by only manipulating the learning scheme without changing the network architecture. The progressive learning scheme is a method to grow the decoder progressively in the train phase. In other words, starting from easier low-resolution layers, it gradually adds high-resolution layers. Although the progressive learning successfullyl captures the context of object, its output boundary tends to be rough. To solve this problem, we also propose a multi-task learning (MTL) scheme that processes the object saliency map and contour in a single network jointly. The proposed MTL scheme trains the network in an edge-preserved direction through an auxiliary branch that learns contours. The proposed a learning scheme can be combined with other convolution block manipulation methods. Extensive experiments on five datasets show that the proposed method performs best compared with state-of-the-art methods in most cases.

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

Olivier Moliner, Sangxia Huang, Kalle Åström

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.

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

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.

Adaptive Estimation of Optimal Color Transformations for Deep Convolutional Network Based Homography Estimation

Miguel A. Molina-Cabello, Jorge García-González, Rafael Marcos Luque-Baena, Karl Thurnhofer-Hemsi, Ezequiel López-Rubio

Auto-TLDR; Improving Homography Estimation from a Pair of Natural Images Using Deep Convolutional Neural Networks

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Homography estimation from a pair of natural images is a problem of paramount importance for computer vision. Specialized deep convolutional neural networks have been proposed to accomplish this task. In this work, a method to enhance the result of this kind of homography estimators is proposed. Our approach generates a set of tentative color transformations for the image pair. Then the color transformed image pairs are evaluated by a regressor that estimates the quality of the homography that would be obtained by supplying the transformed image pairs to the homography estimator. Then the image pair that is predicted to yield the best result is provided to the homography estimator. Experimental results are shown, which demonstrate that our approach performs better than the direct application of the homography estimator to the original image pair, both in qualitative and quantitative terms.

Utilising Visual Attention Cues for Vehicle Detection and Tracking

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

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.

Forground-Guided Vehicle Perception Framework

Kun Tian, Tong Zhou, Shiming Xiang, Chunhong Pan

Auto-TLDR; A foreground segmentation branch for vehicle detection

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As the basis of advanced visual tasks such as vehicle tracking and traffic flow analysis, vehicle detection needs to accurately predict the position and category of vehicle objects. In the past decade, deep learning based methods have made great progress. However, we also notice that some existing cases are not studied thoroughly. First, false positive on the background regions is one of the critical problems. Second, most of the previous approaches only optimize a single vehicle detection model, ignoring the relationship between different visual perception tasks. In response to the above two findings, we introduce a foreground segmentation branch for the first time, which can predict the pixel level of vehicles in advance. Furthermore, two attention modules are designed to guide the work of the detection branch. The proposed method can be easily grafted into the one-stage and two-stage detection framework. We evaluate the effectiveness of our model on LSVH, a dataset with large variations in vehicle scales, and achieve the state-of-the-art detection accuracy.

Point In: Counting Trees with Weakly Supervised Segmentation Network

Pinmo Tong, Shuhui Bu, Pengcheng Han

Auto-TLDR; Weakly Tree counting using Deep Segmentation Network with Localization and Mask Prediction

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For tree counting tasks, since traditional image processing methods require expensive feature engineering and are not end-to-end frameworks, this will cause additional noise and cannot be optimized overall, so this method has not been widely used in recent trends of tree counting application. Recently, many deep learning based approaches are designed for this task because of the powerful feature extracting ability. The representative way is bounding box based supervised method, but time-consuming annotations are indispensable for them. Moreover, these methods are difficult to overcome the occlusion or overlap. To solve this problem, we propose a weakly tree counting network (WTCNet) based on deep segmentation network with only point supervision. It can simultaneously complete tree counting with localization and output mask of each tree at the same time. We first adopt a novel feature extractor network (FENet) to get features of input images, and then an effective strategy is introduced to deal with different mask predictions. In the end, we propose a basic localization guidance accompany with rectification guidance to train the network. We create two different datasets and select an existing challenging plant dataset to evaluate our method on three different tasks. Experimental results show the good performance improvement of our method compared with other existing methods. Further study shows that our method has great potential to reduce human labor and provide effective ground-truth masks and the results show the superiority of our method over the advanced methods.

PA-FlowNet: Pose-Auxiliary Optical Flow Network for Spacecraft Relative Pose Estimation

Zhi Yu Chen, Po-Heng Chen, Kuan-Wen Chen, Chen-Yu Chan

Auto-TLDR; PA-FlowNet: An End-to-End Pose-auxiliary Optical Flow Network for Space Travel and Landing

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During the process of space travelling and space landing, the spacecraft attitude estimation is the indispensable work for navigation. Since there are not enough satellites for GPS-like localization in space, the computer vision technique is adopted to address the issue. The most crucial task for localization is the extraction of correspondences. In computer vision, optical flow estimation is often used for finding correspondences between images. As the deep neural network being more popular in recent years, FlowNet2 has played a vital role which achieves great success. In this paper, we present PA-FlowNet, an end-to-end pose-auxiliary optical flow network which can use the predicted relative camera pose to improve the performance of optical flow. PA-FlowNet is composed of two sub-networks, the foreground-attention flow network and the pose regression network. The foreground-attention flow network is constructed bybased on FlowNet2 model and modified with the proposed foreground-attention approach. We introduced this approach with the concept of curriculum learning for foreground-background segmentation to avoid backgrounds from resulting in flow prediction error. The pose regression network is used to regress the relative camera pose as an auxiliary for increasing the accuracy of the flow estimation. In addition, to simulate the test environment for spacecraft pose estimation, we construct a 64K moon model and to simulate aerial photography with various attitudes to generate Moon64K dataset in this paper. PA-FlowNet significantly outperforms all existing methods on our the proposed Moon64K dataset. Furthermore, we also predict the relative pose via proposed PA-FlowNet and accomplish the remarkable performance.

Visual Object Tracking in Drone Images with Deep Reinforcement Learning

Derya Gözen, Sedat Ozer

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.

Real Time Fencing Move Classification and Detection at Touch Time During a Fencing Match

Cem Ekin Sunal, Chris G. Willcocks, Boguslaw Obara

Auto-TLDR; Fencing Body Move Classification and Detection Using Deep Learning

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Fencing is a fast-paced sport played with swords which are Epee, Foil, and Saber. However, such fast-pace can cause referees to make wrong decisions. Review of slow-motion camera footage in tournaments helps referees’ decision making, but it interrupts the match and may not be available for every organization. Motivated by the need for better decision making, analysis, and availability, we introduce the first fully-automated deep learning classification and detection system for fencing body moves at the moment a touch is made. This is an important step towards creating a fencing analysis system, with player profiling and decision tools that will benefit the fencing community. The proposed architecture combines You Only Look Once version three (YOLOv3) with a ResNet-34 classifier, trained on ImageNet settings to obtain 83.0\% test accuracy on the fencing moves. These results are exciting development in the sport, providing immediate feedback and analysis along with accessibility, hence making it a valuable tool for trainers and fencing match referees.

Object-Oriented Map Exploration and Construction Based on Auxiliary Task Aided DRL

Junzhe Xu, Jianhua Zhang, Shengyong Chen, Honghai Liu

Auto-TLDR; Auxiliary Task Aided Deep Reinforcement Learning for Environment Exploration by Autonomous Robots

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Environment exploration by autonomous robots through deep reinforcement learning (DRL) based methods has attracted more and more attention. However, existing methods usually focus on robot navigation to single or multiple fixed goals, while ignoring the perception and construction of external environments. In this paper, we propose a novel environment exploration task based on DRL, which requires a robot fast and completely perceives all objects of interest, and reconstructs their poses in a global environment map, as much as the robot can do. To this end, we design an auxiliary task aided DRL model, which is integrated with the auxiliary object detection and 6-DoF pose estimation components. The outcome of auxiliary tasks can improve the learning speed and robustness of DRL, as well as the accuracy of object pose estimation. Comprehensive experimental results on the indoor simulation platform AI2-THOR have shown the effectiveness and robustness of our method.