3D Audio-Visual Speaker Tracking with a Novel Particle Filter

Hong Liu, Yongheng Sun, Yidi Li, Bing Yang

Responsive image

Auto-TLDR; 3D audio-visual speaker tracking using particle filter based method

Slides Poster

3D speaker tracking using co-located audio-visual sensors has received much attention recently. Though various methods have been attempted to this field, it is still challenging to obtain a reliable 3D tracking result since the position of co-located sensors are restricted to a small area. In this paper, a novel particle filter (PF) based method is proposed for 3D audio-visual speaker tracking. Compared with traditional PF based audio-visual speaker tracking method, our 3D audio-visual tracker has two main characteristics. In the prediction stage, we use audio-visual information at current frame to further adjust the direction of the particles after the particle state transition process, which can make the particles more concentrated around the speaker direction. In the update stage, the particle likelihood is calculated by fusing both the visual distance and audio-visual direction information. Specially, the distance likelihood is obtained according to the camera projection model and the adaptively estimated size of speaker face or head, and the direction likelihood is determined by audio-visual particle fitness. In this way, the particle likelihood can better represent the speaker presence probability in 3D space. Experimental results show that the proposed tracker outperforms other methods and provides a favorable speaker tracking performance both in 3D space and on the image plane.

Similar papers

Audio-Visual Speech Recognition Using a Two-Step Feature Fusion Strategy

Hong Liu, Wanlu Xu, Bing Yang

Responsive image

Auto-TLDR; A Two-Step Feature Fusion Network for Speech Recognition

Slides Poster Similar

Lip-reading methods and fusion strategy are crucial for audio-visual speech recognition. In recent years, most approaches involve two separate audio and visual streams with early or late fusion strategies. Such a single-stage fusion method may fail to guarantee the integrity and representativeness of fusion information simultaneously. This paper extends a traditional single-stage fusion network to a two-step feature fusion network by adding an audio-visual early feature fusion (AV-EFF) stream to the baseline model. This method can learn the fusion information of different stages, preserving the original features as much as possible and ensuring the independence of different features. Besides, to capture long-range dependencies of video information, a non-local block is added to the feature extraction part of the visual stream (NL-Visual) to obtain the long-term spatio-temporal features. Experimental results on the two largest public datasets in English (LRW) and Mandarin (LRW-1000) demonstrate our method is superior to other state-of-the-art methods.

Robust Audio-Visual Speech Recognition Based on Hybrid Fusion

Hong Liu, Wenhao Li, Bing Yang

Responsive image

Auto-TLDR; Hybrid Fusion Based AVSR with Residual Networks and Bidirectional Gated Recurrent Unit for Robust Speech Recognition in Noise Conditions

Slides Poster Similar

The fusion of audio and visual modalities is an important stage of audio-visual speech recognition (AVSR), which is generally approached through feature fusion or decision fusion. Feature fusion can exploit the covariations between features from different modalities effectively, whereas decision fusion shows the robustness of capturing an optimal combination of multi-modality. In this work, to take full advantage of the complementarity of the two fusion strategies and address the challenge of inherent ambiguity in noisy environments, we propose a novel hybrid fusion based AVSR method with residual networks and Bidirectional Gated Recurrent Unit (BGRU), which is able to distinguish homophones in both clean and noisy conditions. Specifically, a simple yet effective audio-visual encoder is used to map audio and visual features into a shared latent space to capture more discriminative multi-modal feature and find the internal correlation between spatial-temporal information for different modalities. Furthermore, a decision fusion module is designed to get final predictions in order to robustly utilize the reliability measures of audio-visual information. Finally, we introduce a combined loss, which shows its noise-robustness in learning the joint representation across various modalities. Experimental results on the largest publicly available dataset (LRW) demonstrate the robustness of the proposed method under various noisy conditions.

Audio-Video Detection of the Active Speaker in Meetings

Francisco Madrigal, Frederic Lerasle, Lionel Pibre, Isabelle Ferrané

Responsive image

Auto-TLDR; Active Speaker Detection with Visual and Contextual Information from Meeting Context

Slides Poster Similar

Meetings are a common activity that provides certain challenges when creating systems that assist them. Such is the case of the Active speaker detection, which can provide useful information for human interaction modeling, or human-robot interaction. Active speaker detection is mostly done using speech, however, certain visual and contextual information can provide additional insights. In this paper we propose an active speaker detection framework that integrates audiovisual features with social information, from the meeting context. Visual cue is processed using a Convolutional Neural Network (CNN) that captures the spatio-temporal relationships. We analyze several CNN architectures with both cues: raw pixels (RGB images) and motion (estimated with optical flow). Contextual reasoning is done with an original methodology, based on the gaze of all participants. We evaluate our proposal with a public \textcolor{black}{benchmark} in state-of-art: AMI corpus. We show how the addition of visual and context information improves the performance of the active speaker detection.

Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Jianrong Wang, Tong Wu, Shanyu Wang, Mei Yu, Qiang Fang, Ju Zhang, Li Liu

Responsive image

Auto-TLDR; Lip Motion Network for Text-Independent and Text-Dependent Speaker Recognition

Slides Poster Similar

Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two dimensional (2D) lip images to recognize speaker in a text-dependent context. However, 2D lip easily suffers from face orientations. To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts. A novel regional feedback module (RFM) is proposed to explore attentions in different lip regions. Besides, prior knowledge of lip motion is investigated to complement RFM, where landmark-level and frame-level features are merged to form a better feature representation. Moreover, we present two methods, i.e., coordinate transformation and face posture correction to pre-process the LSD-AV dataset, which contains 68 speakers and 146 sentences per speaker. The evaluation results on this dataset demonstrate that our proposed 3LMNet is superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and outperforms the state-of-the-art using 2D lip image as well as the 3D face. The code of this work is released at https://github.com/wutong18/Three-Dimensional-Lip-Motion-Ne twork-for-Text-Independent-Speaker-Recognition.

Mutual Alignment between Audiovisual Features for End-To-End Audiovisual Speech Recognition

Hong Liu, Yawei Wang, Bing Yang

Responsive image

Auto-TLDR; Mutual Iterative Attention for Audio Visual Speech Recognition

Slides Poster Similar

Asynchronization issue caused by different types of modalities is one of the major problems in audio visual speech recognition (AVSR) research. However, most AVSR systems merely rely on up sampling of video or down sampling of audio to align audio and visual features, assuming that the feature sequences are aligned frame-by-frame. These pre-processing steps oversimplify the asynchrony relation between acoustic signal and lip motion, lacking flexibility and impairing the performance of the system. Although there are systems modeling the asynchrony between the modalities, sometimes they fail to align speech and video precisely in some even all noise conditions. In this paper, we propose a mutual feature alignment method for AVSR which can make full use of cross modility information to address the asynchronization issue by introducing Mutual Iterative Attention (MIA) mechanism. Our method can automatically learn an alignment in a mutual way by performing mutual attention iteratively between the audio and visual features, relying on the modified encoder structure of Transformer. Experimental results show that our proposed method obtains absolute improvements up to 20.42% over the audio modality alone depending upon the signal-to-noise-ratio (SNR) level. Better recognition performance can also be achieved comparing with the traditional feature concatenation method under both clean and noisy conditions. It is expectable that our proposed mutual feature alignment method can be easily generalized to other multimodal tasks with semantically correlated information.

Spatial Bias in Vision-Based Voice Activity Detection

Kalin Stefanov, Mohammad Adiban, Giampiero Salvi

Responsive image

Auto-TLDR; Spatial Bias in Vision-based Voice Activity Detection in Multiparty Human-Human Interactions

Poster Similar

We present models for automatic vision-based voice activity detection (VAD) in multiparty human-human interactions that are aimed at complementing the acoustic VAD methods. We provide evidence that this type of vision-based VAD models are susceptible to spatial bias in the datasets. The physical settings of the interaction, usually constant throughout data acquisition, determines the distribution of head poses of the participants. Our results show that when the head pose distributions are significantly different in the training and test sets, the performance of the models drops significantly. This suggests that previously reported results on datasets with a fixed physical configuration may overestimate the generalization capabilities of this type of models. We also propose a number of possible remedies to the spatial bias, including data augmentation, input masking and dynamic features, and provide an in-depth analysis of the visual cues used by our models.

Visual Saliency Oriented Vehicle Scale Estimation

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

Responsive image

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

Slides Poster Similar

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.

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

Chengliang Wang, Xin Xie, Chao Liao

Responsive image

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

Slides Poster Similar

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.

DAL: A Deep Depth-Aware Long-Term Tracker

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

Responsive image

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

Slides Poster Similar

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.

Utilising Visual Attention Cues for Vehicle Detection and Tracking

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

Responsive image

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

Slides Poster Similar

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.

Improving Mix-And-Separate Training in Audio-Visual Sound Source Separation with an Object Prior

Quan Nguyen, Simone Frintrop, Timo Gerkmann, Mikko Lauri, Julius Richter

Responsive image

Auto-TLDR; Object-Prior: Learning the 1-to-1 correspondence between visual and audio signals by audio- visual sound source methods

Slides Similar

The performance of an audio-visual sound source separation system is determined by its ability to separate audio sources given images of the sources and the audio mixture. The goal of this study is to investigate the ability to learn the mapping between the sounds and the images of instruments by audio- visual sound source separation methods based on the state-of-the- art PixelPlayer [1]. Theoretical and empirical analyses illustrate that the PixelPlayer is not properly trained to learn the 1-to- 1 correspondence between visual and audio signals during its mix-and-separate training process. Based on the insights from this analysis, a weakly-supervised method called Object-Prior is proposed and evaluated on two audio-visual datasets. The experimental results show that the proposed Object-Prior method outperforms the PixelPlayer and other baselines in the audio- visual sound source separation task. It is also more robust against asynchronized data, where the frame and the audio do not come from the same video, and recognizes musical instruments based on their sound with higher accuracy than the PixelPlayer. This indicates that learning the 1-to-1 correspondence between visual and audio features of an instrument improves the effectiveness of audio-visual sound source separation.

TSDM: Tracking by SiamRPN++ with a Depth-Refiner and a Mask-Generator

Pengyao Zhao, Quanli Liu, Wei Wang, Qiang Guo

Responsive image

Auto-TLDR; TSDM: A Depth-D Tracker for 3D Object Tracking

Slides Poster Similar

In a generic object tracking, depth (D) information provides informative cues for foreground-background separation and target bounding box regression. However, so far, few trackers have used depth information to play the important role aforementioned due to the lack of a suitable model. In this paper, a RGB-D tracker named TSDM is proposed, which is composed of a Mask-generator (M-g), SiamRPN++ and a Depth-refiner (D-r). The M-g generates the background masks, and updates them as the target 3D position changes. The D-r optimizes the target bounding box estimated by SiamRPN++, based on the spatial depth distribution difference between the target and the surrounding background. Extensive evaluation on the Princeton Tracking Benchmark and the Visual Object Tracking challenge shows that our tracker outperforms the state-of-the-art by a large margin while achieving 23 FPS. In addition, a light-weight variant can run at 31 FPS and thus it is practical for real world applications. Code and models of TSDM are available at https://github.com/lql-team/TSDM.

Learning Visual Voice Activity Detection with an Automatically Annotated Dataset

Stéphane Lathuiliere, Pablo Mesejo, Radu Horaud

Responsive image

Auto-TLDR; Deep Visual Voice Activity Detection with Optical Flow

Slides Similar

Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. V-VAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing V-VAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets in-the-wild, based on combining A-VAD and face detection. A thorough empirical evaluation shows the advantage of training the proposed deep V-VAD models with such a dataset.

Efficient Correlation Filter Tracking with Adaptive Training Sample Update Scheme

Shan Jiang, Shuxiao Li, Chengfei Zhu, Nan Yan

Responsive image

Auto-TLDR; Adaptive Training Sample Update Scheme of Correlation Filter Based Trackers for Visual Tracking

Slides Poster Similar

Visual tracking serves as a significant module in many applications. However, the heavy computation and low speed of many recent trackers restrict their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter based trackers limits their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter based trackers and propose an efficient and adaptive training sample update scheme. Training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with difference hashing algorithm(DHA) or discarded according to tracking result reliability. Experiments on OTB-2015, Temple Color 128 and UAV123 demonstrate our tracker performs favourably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2GHz).

Detection of Calls from Smart Speaker Devices

Vinay Maddali, David Looney, Kailash Patil

Responsive image

Auto-TLDR; Distinguishing Between Smart Speaker and Cell Devices Using Only the Audio Using a Feature Set

Slides Poster Similar

The ubiquity of smart speakers is increasing, with a growing number of households utilising these devices to make calls over the telephony network. As the technology is typically configured to retain the cellular phone number of the user, it presents challenges in applications where knowledge of the true call origin is required. There are a wide range of makes and models for these devices, as is the case with cell phones, and it is challenging to detect the general category as a smart speaker or cell, independent of the designated phone number. In this paper, we present an approach to differentiate between calls originating from smart speakers and ones from cellular devices using only the audio. We present a feature set that characterises the relevant acoustic information, such as the degree of reverberation and noise, to distinguish between these categories. When evaluated on a dataset spanning multiple models for each device category, as well as different modes-of-usage and microphone-speaker distances, the method yields an Equal Error Rate (EER) of 12.6%.

Single-Modal Incremental Terrain Clustering from Self-Supervised Audio-Visual Feature Learning

Reina Ishikawa, Ryo Hachiuma, Akiyoshi Kurobe, Hideo Saito

Responsive image

Auto-TLDR; Multi-modal Variational Autoencoder for Terrain Type Clustering

Slides Poster Similar

The key to an accurate understanding of terrain is to extract the informative features from the multi-modal data obtained from different devices. Sensors, such as RGB cameras, depth sensors, vibration sensors, and microphones, are used as the multi-modal data. Many studies have explored ways to use them, especially in the robotics field. Some papers have successfully introduced single-modal or multi-modal methods. However, in practice, robots can be faced with extreme conditions; microphones do not work well in the crowded scenes, and an RGB camera cannot capture terrains well in the dark. In this paper, we present a novel framework using the multi-modal variational autoencoder and the Gaussian mixture model clustering algorithm on image data and audio data for terrain type clustering. Our method enables the terrain type clustering even if one of the modalities (either image or audio) is missing at the test-time. We evaluated the clustering accuracy with a conventional multi-modal terrain type clustering method and we conducted ablation studies to show the effectiveness of our approach.

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

Dmitrii Matveichev, Daw-Tung Lin

Responsive image

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

Slides Poster Similar

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.

Adaptive Context-Aware Discriminative Correlation Filters for Robust Visual Object Tracking

Tianyang Xu, Zhenhua Feng, Xiaojun Wu, Josef Kittler

Responsive image

Auto-TLDR; ACA-DCF: Adaptive Context-Aware Discriminative Correlation Filter with complementary attention mechanisms

Slides Poster Similar

In recent years, Discriminative Correlation Filters (DCFs) have gained popularity due to their superior performance in visual object tracking. However, existing DCF trackers usually learn filters using fixed attention mechanisms that focus on the centre of an image and suppresses filter amplitudes in surroundings. In this paper, we propose an Adaptive Context-Aware Discriminative Correlation Filter (ACA-DCF) that is able to improve the existing DCF formulation with complementary attention mechanisms. Our ACA-DCF integrates foreground attention and background attention for complementary context-aware filter learning. More importantly, we ameliorate the design using an adaptive weighting strategy that takes complex appearance variations into account. The experimental results obtained on several well-known benchmarks demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art approaches.

Unsupervised Sound Source Localization From Audio-Image Pairs Using Input Gradient Map

Tomohiro Tanaka, Takahiro Shinozaki

Responsive image

Auto-TLDR; Unsupervised Sound Localization Using Gradient Method

Slides Poster Similar

Humans easily and routinely identify an image region that corresponds to an observed sound in their daily lives. The task is formulated as an unsupervised sound source localization without using tagged data. Recently, several methods have been proposed that utilize the activation of hidden or output layers of neural networks, such as an attention layer or feature maps in a convolutional neural network (CNN). We propose another strategy that obtains a localization map at the input side, applying the widely used input gradient method. It is computationally efficient and can be easily applied to any existing techniques because it is free from the network structure. Taking advantage of it, we propose a combination method with existing methods for higher sound localization performance. Experiments are performed using the Flickr-SoundNet data set. When a pre-trained image front-end was used, the proposed method gives better results than the attention-based method. For a completely unsupervised condition, the gradient method provides comparable performance as the conventional methods; the best results are obtained by this combination method.

Person Recognition with HGR Maximal Correlation on Multimodal Data

Yihua Liang, Fei Ma, Yang Li, Shao-Lun Huang

Responsive image

Auto-TLDR; A correlation-based multimodal person recognition framework that learns discriminative embeddings of persons by joint learning visual features and audio features

Slides Poster Similar

Multimodal person recognition is a common task in video analysis and public surveillance, where information from multiple modalities, such as images and audio extracted from videos, are used to jointly determine the identity of a person. Previous person recognition techniques either use only uni-modal data or only consider shared representations between different input modalities, while leaving the extraction of their relationship with identity information to downstream tasks. Furthermore, real-world data often contain noise, which makes recognition more challenging practical situations. In our work, we propose a novel correlation-based multimodal person recognition framework that is relatively simple but can efficaciously learn supervised information in multimodal data fusion and resist noise. Specifically, our framework learns a discriminative embeddings of persons by joint learning visual features and audio features while maximizing HGR maximal correlation among multimodal input and persons' identities. Experiments are done on a subset of Voxceleb2. Compared with state-of-the-art methods, the proposed method demonstrates an improvement of accuracy and robustness to noise.

Are Multiple Cross-Correlation Identities Better Than Just Two? Improving the Estimate of Time Differences-Of-Arrivals from Blind Audio Signals

Danilo Greco, Jacopo Cavazza, Alessio Del Bue

Responsive image

Auto-TLDR; Improving Blind Channel Identification Using Cross-Correlation Identity for Time Differences-of-Arrivals Estimation

Slides Poster Similar

Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior "blind" works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while always sticking to the case N = 2 microphones. But what if we can obtain a direct improvement in performance by just increasing N? In this paper we try to investigate this direction, showing that, despite the arguable simplicity, this is capable of (sharply) improving upon state-of-the-art blind channel identification methods based on CCI, without modifying the computational pipeline. Inspired by our results, we seek to warm up the community and the practitioners by paving the way (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements.

A Neural Lip-Sync Framework for Synthesizing Photorealistic Virtual News Anchors

Ruobing Zheng, Zhou Zhu, Bo Song, Ji Changjiang

Responsive image

Auto-TLDR; Lip-sync: Synthesis of a Virtual News Anchor for Low-Delayed Applications

Slides Poster Similar

Lip sync has emerged as a promising technique to generate mouth movements from audio signals. However, synthesizing a high-resolution and photorealistic virtual news anchor with current methods is still challenging. The lack of natural appearance, visual consistency, and processing efficiency is the main issue. In this paper, we present a novel lip-sync framework specially designed for producing a virtual news anchor for a target person. A pair of Temporal Convolutional Networks are used to learn the seq-to-seq mapping from audio signals to mouth movements, followed by a neural rendering model that translates the intermediate face representation to the high-quality appearance. This fully-trainable framework avoids several time-consuming steps in traditional graphics-based methods, meeting the requirements of many low-delay applications. Experiments show that our method has advantages over modern neural-based methods in both visual appearance and processing efficiency.

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

Responsive image

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

Slides Poster Similar

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.

Unsupervised Co-Segmentation for Athlete Movements and Live Commentaries Using Crossmodal Temporal Proximity

Yasunori Ohishi, Yuki Tanaka, Kunio Kashino

Responsive image

Auto-TLDR; A guided attention scheme for audio-visual co-segmentation

Slides Poster Similar

Audio-visual co-segmentation is a task to extract segments and regions corresponding to specific events on unlabelled audio and video signals. It is particularly important to accomplish it in an unsupervised way, since it is generally very difficult to manually label all the objects and events appearing in audio-visual signals for supervised learning. Here, we propose to take advantage of temporal proximity of corresponding audio and video entities included in the signals. For this purpose, we newly employ a guided attention scheme to this task to efficiently detect and utilize temporal cooccurrences of audio and video information. The experiments using a real TV broadcasting of Sumo wrestling, a sport event, with live commentaries show that our model can automatically extract specific athlete movements and its spoken descriptions in an unsupervised manner.

Siamese Fully Convolutional Tracker with Motion Correction

Mathew Francis, Prithwijit Guha

Responsive image

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

Slides Poster Similar

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.

Let's Play Music: Audio-Driven Performance Video Generation

Hao Zhu, Yi Li, Feixia Zhu, Aihua Zheng, Ran He

Responsive image

Auto-TLDR; APVG: Audio-driven Performance Video Generation Using Structured Temporal UNet

Slides Poster Similar

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to achieve this new task to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap offers more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and inter-frame temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

RSINet: Rotation-Scale Invariant Network for Online Visual Tracking

Yang Fang, Geunsik Jo, Chang-Hee Lee

Responsive image

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

Slides Poster Similar

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.

Hybrid Network for End-To-End Text-Independent Speaker Identification

Wajdi Ghezaiel, Luc Brun, Olivier Lezoray

Responsive image

Auto-TLDR; Text-Independent Speaker Identification with Scattering Wavelet Network and Convolutional Neural Networks

Slides Poster Similar

Deep learning has recently improved the performance of Speaker Identification (SI) systems. Promising results have been obtained with Convolutional Neural Networks (CNNs). This success are mostly driven by the advent of large datasets. However in the context of commercial applications, collection of large amount of training data is not always possible. In addition, robustness of a SI system is adversely effected by short utterances. SI with only a few and short utterances is a challenging problem. Therefore, in this paper, we propose a novel text-independent speaker identification system. The proposed system can identify speakers by learning from only few training short utterances examples. To achieve this, we combine CNN with Scattering Wavelet Network. We propose a two-stage feature extraction framework using a two-layer wavelet scattering network coupled with a CNN for SI system. The proposed architecture takes variable length speech segments. To evaluate the effectiveness of the proposed approach, Timit and Librispeech datasets are used in the experiments. These conducted experiments show that our hybrid architecture performs successfully for SI, even with a small number and short duration of training samples. In comparaison with related methods, the obtained results shows that an hybrid architecture achieve better performance.

Real-Time Drone Detection and Tracking with Visible, Thermal and Acoustic Sensors

Fredrik Svanström, Cristofer Englund, Fernando Alonso-Fernandez

Responsive image

Auto-TLDR; Automatic multi-sensor drone detection using sensor fusion

Slides Poster Similar

This paper explores the process of designing an automatic multi-sensor drone detection system. Besides the common video and audio sensors, the system also includes a thermal infrared camera, which is shown to be a feasible solution to the drone detection task. Even with slightly lower resolution, the performance is just as good as a camera in visible range. The detector performance as a function of the sensor-to-target distance is also investigated. In addition, using sensor fusion, the system is made more robust than the individual sensors, helping to reduce false detections. To counteract the lack of public datasets, a novel video dataset containing 650 annotated infrared and visible videos of drones, birds, airplanes and helicopters is also presented. The database is complemented with an audio dataset of the classes drones, helicopters and background noise.

NetCalib: A Novel Approach for LiDAR-Camera Auto-Calibration Based on Deep Learning

Shan Wu, Amnir Hadachi, Damien Vivet, Yadu Prabhakar

Responsive image

Auto-TLDR; Automatic Calibration of LiDAR and Cameras using Deep Neural Network

Slides Poster Similar

A fusion of LiDAR and cameras have been widely used in many robotics applications such as classification, segmentation, object detection, and autonomous driving. It is essential that the LiDAR sensor can measure distances accurately, which is a good complement to the cameras. Hence, calibrating sensors before deployment is a mandatory step. The conventional methods include checkerboards, specific patterns, or human labeling, which is trivial and human-labor extensive if we do the same calibration process every time. The main propose of this research work is to build a deep neural network that is capable of automatically finding the geometric transformation between LiDAR and cameras. The results show that our model manages to find the transformations from randomly sampled artificial errors. Besides, our work is open-sourced for the community to fully utilize the advances of the methodology for developing more the approach, initiating collaboration, and innovation in the topic.

Audio-Visual Predictive Coding for Self-Supervised Visual Representation Learning

Mani Kumar Tellamekala, Michel Valstar, Michael Pound, Timo Giesbrecht

Responsive image

Auto-TLDR; AV-PPC: A Multi-task Learning Framework for Learning Semantic Visual Features from Unlabeled Video Data

Slides Poster Similar

Self-supervised learning has emerged as a candidate approach to learn semantic visual features from unlabeled video data. In self-supervised learning, intrinsic correspondences between data points are used to define a proxy task that forces the model to learn semantic representations. Most existing proxy tasks applied to video data exploit only either intra-modal (e.g. temporal) or cross-modal (e.g. audio-visual) correspondences separately. In theory, jointly learning both these correspondences may result in richer visual features; but, as we show in this work, doing so is non-trivial in practice. To address this problem, we introduce `Audio-Visual Permutative Predictive Coding' (AV-PPC), a multi-task learning framework designed to fully leverage the temporal and cross-modal correspondences as natural supervision signals. In AV-PPC, the model is trained to simultaneously learn multiple intra- and cross-modal predictive coding sub-tasks. By using visual speech recognition (lip-reading) as the downstream evaluation task, we show that our proposed proxy task can learn higher quality visual features than existing proxy tasks. We also show that AV-PPC visual features are highly data-efficient. Without further finetuning, AV-PPC visual encoder achieves 80.30% spoken word classification rate on the LRW dataset, performing on par with directly or fully supervised visual encoders learned from large amounts of labeled data.

Digit Recognition Applied to Reconstructed Audio Signals Using Deep Learning

Anastasia-Sotiria Toufa, Constantine Kotropoulos

Responsive image

Auto-TLDR; Compressed Sensing for Digit Recognition in Audio Reconstruction

Poster Similar

Compressed sensing allows signal reconstruction from a few measurements. This work proposes a complete pipeline for digit recognition applied to audio reconstructed signals. The reconstruction procedure exploits the assumption that the original signal lies in the range of a generator. A pretrained generator of a Generative Adversarial Network generates audio digits. A new method for reconstruction is proposed, using only the most active segment of the signal, i.e., the segment with the highest energy. The underlying assumption is that such segment offers a more compact representation, preserving the meaningful content of signal. Cases when the reconstruction produces noise, instead of digit, are treated as outliers. In order to detect and reject them, three unsupervised indicators are used, namely, the total energy of reconstructed signal, the predictions of an one-class Support Vector Machine, and the confidence of a pretrained classifier used for recognition. This classifier is based on neural networks architectures and is pretrained on original audio recordings, employing three input representations, i.e., raw audio, spectrogram, and gammatonegram. Experiments are conducted, analyzing both the quality of reconstruction and the performance of classifiers in digit recognition, demonstrating that the proposed method yields higher performance in both the quality of reconstruction and digit recognition accuracy.

Rotational Adjoint Methods for Learning-Free 3D Human Pose Estimation from IMU Data

Caterina Emilia Agelide Buizza, Yiannis Demiris

Responsive image

Auto-TLDR; Learning-free 3D Human Pose Estimation from Inertial Measurement Unit Data

Poster Similar

We present a new framework for learning-free 3D human pose estimation from Inertial Measurement Unit (IMU) data. The proposed method does not rely on a full motion sequence to calculate a pose for any particular time point and thus can operate in real-time. A cost function based only on joint rotations is used, removing the need for frequent transformations between rotations and 3D Cartesian coordinates. A Jacobian that preserves skeleton structure is derived using Adjoint methods from Variational Data Assimilation. To facilitate further research in IMU-based Motion Capture, we provide a dataset that combines RGB and depth images from an Intel RealSense camera, marker-based motion capture from an Optitrack system and Xsens IMU data. We have evaluated our method on both our dataset and the Total Capture dataset, showing an average error across 24 joints of 0.45 and 0.48 radians respectively.

Total Estimation from RGB Video: On-Line Camera Self-Calibration, Non-Rigid Shape and Motion

Antonio Agudo

Responsive image

Auto-TLDR; Joint Auto-Calibration, Pose and 3D Reconstruction of a Non-rigid Object from an uncalibrated RGB Image Sequence

Slides Poster Similar

In this paper we present a sequential approach to jointly retrieve camera auto-calibration, camera pose and the 3D reconstruction of a non-rigid object from an uncalibrated RGB image sequence, without assuming any prior information about the shape structure, nor the need for a calibration pattern, nor the use of training data at all. To this end, we propose a Bayesian filtering approach based on a sum-of-Gaussians filter composed of a bank of extended Kalman filters (EKF). For every EKF, we make use of dynamic models to estimate its state vector, which later will be Gaussianly combined to achieve a global solution. To deal with deformable objects, we incorporate a mechanical model solved by using the finite element method. Thanks to these ingredients, the resulting method is both efficient and robust to several artifacts such as missing and noisy observations as well as sudden camera motions, while being available for a wide variety of objects and materials, including isometric and elastic shape deformations. Experimental validation is proposed in real experiments, showing its strengths with respect to competing approaches.

Visual Object Tracking in Drone Images with Deep Reinforcement Learning

Derya Gözen, Sedat Ozer

Responsive image

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

Slides Poster Similar

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.

Unsupervised Moving Object Detection through Background Models for PTZ Camera

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

Responsive image

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

Slides Poster Similar

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.

AV-SLAM: Autonomous Vehicle SLAM with Gravity Direction Initialization

Kaan Yilmaz, Baris Suslu, Sohini Roychowdhury, L. Srikar Muppirisetty

Responsive image

Auto-TLDR; VI-SLAM with AGI: A combination of three SLAM algorithms for autonomous vehicles

Slides Poster Similar

Simultaneous localization and mapping (SLAM) algorithms that are aimed at autonomous vehicles (AVs) are required to utilize sensor redundancies specific to AVs and enable accurate, fast and repeatable estimations of pose and path trajectories. In this work, we present a combination of three SLAM algorithms that utilize a different subset of available sensors such as inertial measurement unit (IMU), a gray-scale mono-camera, and a Lidar. Also, we propose a novel acceleration-based gravity direction initialization (AGI) method for the visual-inertial SLAM algorithm. We analyze the SLAM algorithms and initialization methods for pose estimation accuracy, speed of convergence and repeatability on the KITTI odometry sequences. The proposed VI-SLAM with AGI method achieves relative pose errors less than 2\%, convergence in half a minute or less and convergence time variability less than 3s, which makes it preferable for AVs.

Which are the factors affecting the performance of audio surveillance systems?

Antonio Greco, Antonio Roberto, Alessia Saggese, Mario Vento

Responsive image

Auto-TLDR; Sound Event Recognition Using Convolutional Neural Networks and Visual Representations on MIVIA Audio Events

Slides Similar

Sound event recognition systems are rapidly becoming part of our life, since they can be profitably used in several vertical markets, ranging from audio security applications to scene classification and multi-modal analysis in social robotics. In the last years, a not negligible part of the scientific community started to apply Convolutional Neural Networks (CNNs) to image-based representations of the audio stream, due to their successful adoption in almost all the computer vision tasks. In this paper, we carry out a detailed benchmark of various widely used CNN architectures and visual representations on a popular dataset, namely the MIVIA Audio Events database. Our analysis is aimed at understanding how these factors affect the sound event recognition performance with a particular focus on the false positive rate, very relevant in audio surveillance solutions. In fact, although most of the proposed solutions achieve a high recognition rate, the capability of distinguishing the events-of-interest from the background is often not yet sufficient for real systems, and prevent its usage in real applications. Our comprehensive experimental analysis investigates this aspect and allows to identify useful design guidelines for increasing the specificity of sound event recognition systems.

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

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

Responsive image

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

Slides Poster Similar

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.

DenseRecognition of Spoken Languages

Jaybrata Chakraborty, Bappaditya Chakraborty, Ujjwal Bhattacharya

Responsive image

Auto-TLDR; DenseNet: A Dense Convolutional Network Architecture for Speech Recognition in Indian Languages

Slides Poster Similar

In the present study, we have, for the first time, con- sidered a large number of Indian languages for recog- nition from their audio signals of different sources. A dense convolutional network architecture (DenseNet) has been proposed for this classification problem. Dy- namic elimination of low energy frames from the input speech signal has been considered as a preprocessing operation. Mel-spectrogram of pre-processed speech signal is fed to a DenseNet architecture for recogni- tion of its language. Recognition performance of the proposed architecture has been compared with that of several state-of-the-art deep architectures which include a traditional convolutional neural network (CNN), multiple ResNet architectures, CNN-BLSTM and DenseNet-BLSTM hybrid architectures. Addition- ally, we obtained recognition performances of a stacked BLSTM architecture fed with different sets of hand- crafted features for comparison purpose. Simulations have been performed on two different standard datasets which include (i) IITKGP-MLILSC dataset of news clips in 27 different Indian languages and (ii) Linguistic Data Consortium (LDC) dataset of telephonic conver- sations in 5 different Indian languages. Recognition performance of the proposed framework has been found to be consistently and significantly better than all other frameworks implemented in this study.

Model Decay in Long-Term Tracking

Efstratios Gavves, Ran Tao, Deepak Gupta, Arnold Smeulders

Responsive image

Auto-TLDR; Model Bias in Long-Term Tracking

Slides Poster Similar

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.

Siamese Dynamic Mask Estimation Network for Fast Video Object Segmentation

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

Responsive image

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

Slides Poster Similar

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.

Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze

Dongfang Liu, Yiming Cui, Xiaolei Guo, Wei Ding, Baijian Yang, Yingjie Chen

Responsive image

Auto-TLDR; Feature Voting for Robust Visual Localization in Urban Settings

Slides Poster Similar

Accurate localization is a foundational capacity, required for autonomous vehicles to accomplish other tasks such as navigation or path planning. It is a common practice for vehicles to use GPS to acquire location information. However, the application of GPS can result in severe challenges when vehicles run within the inner city where different kinds of structures may shadow the GPS signal and lead to inaccurate location results. To address the localization challenges of urban settings, we propose a novel feature voting technique for visual localization. Different from the conventional front-view-based method, our approach employs views from three directions (front, left, and right) and thus significantly improves the robustness of location prediction. In our work, we craft the proposed feature voting method into three state-of-the-art visual localization networks and modify their architectures properly so that they can be applied for vehicular operation. Extensive field test results indicate that our approach can predict location robustly even in challenging inner-city settings. Our research sheds light on using the visual localization approach to help autonomous vehicles to find accurate location information in a city maze, within a desirable time constraint.

Toward Text-Independent Cross-Lingual Speaker Recognition Using English-Mandarin-Taiwanese Dataset

Yi-Chieh Wu, Wen-Hung Liao

Responsive image

Auto-TLDR; Cross-lingual Speech for Biometric Recognition

Poster Similar

Over 40% of the world's population is bilingual. Existing speaker identification/verification systems, however, assume the same language type for both enrollment and recognition stages. In this work, we investigate the feasibility of employing multilingual speech for biometric application. We establish a dataset containing audio recorded in English, Mandarin and Taiwanese. Three acoustic features, namely, i-vector, d-vector and x-vector have been evaluated for both speaker verification (SV) and identification (SI) tasks. Preliminary experimental results indicate that x-vector achieves the best overall performance. Additionally, model trained with hybrid data demonstrates highest accuracy associated with the cost of data collection efforts. In SI tasks, we obtained over 91\% cross-lingual accuracy all models using 3-second audio. In SV tasks, the EER among cross-lingual test is at most 6.52\%, which is observed on the model trained by English corpus. The outcome suggests the feasibility of adopting cross-lingual speech in building text-independent speaker recognition systems.

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

Responsive image

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

Slides Similar

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.

Talking Face Generation Via Learning Semantic and Temporal Synchronous Landmarks

Aihua Zheng, Feixia Zhu, Hao Zhu, Mandi Luo, Ran He

Responsive image

Auto-TLDR; A semantic and temporal synchronous landmark learning method for talking face generation

Slides Poster Similar

Abstract—Given a speech clip and facial image, the goal of talking face generation is to synthesize a talking face video with accurate mouth synchronization and natural face motion. Recent progress has proven the effectiveness of the landmarks as the intermediate information during talking face generation. However,the large gap between audio and visual modalities makes the prediction of landmarks challenging and limits generation ability. This paper proposes a semantic and temporal synchronous landmark learning method for talking face generation. First, we propose to introduce a word detector to enforce richer semantic information. Then, we propose to preserve the temporal synchronization and consistency between landmarks and audio via the proposed temporal residual loss. Lastly, we employ a U-Net generation network with adaptive reconstruction loss to generate facial images for the predicted landmarks. Experimental results on two benchmark datasets LRW and GRID demonstrate the effectiveness of our model compared to the state-of-the-art methods of talking face generation.

Reducing False Positives in Object Tracking with Siamese Network

Takuya Ogawa, Takashi Shibata, Shoji Yachida, Toshinori Hosoi

Responsive image

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

Slides Poster Similar

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.

Accurate Background Subtraction Using Dynamic Object Presence Probability in Sports Scenes

Ryosuke Watanabe, Jun Chen, Tomoaki Konno, Sei Naito

Responsive image

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

Slides Poster Similar

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.