Gait recognition is an important recognition technology, because gait is not easy to camouflage and does not need cooperation to recognize subjects. However, many existing methods are inadequate in preserving both temporal information and fine-grained information, thus reducing its discrimination. This problem is more serious when the subjects with similar walking postures are identified. In this paper, we try to enhance the discrimination of spatio-temporal gait features from two aspects: effective extraction of spatio-temporal gait features and reasonable refinement of extracted features. Thus our method is proposed, it consists of Spatio-temporal Feature Extraction (SFE) and Global Distance Alignment (GDA). SFE uses Temporal Feature Fusion (TFF) and Fine-grained Feature Extraction (FFE) to effectively extract the spatio-temporal features from raw silhouettes. GDA uses a large number of unlabeled gait data in real life as a benchmark to refine the extracted spatio-temporal features. GDA can make the extracted features have low inter-class similarity and high intra-class similarity, thus enhancing their discrimination. Extensive experiments on mini-OUMVLP and CASIA-B have proved that we have a better result than some state-of-the-art methods.
Harcourt Butler Technological Institute（哈考特·巴特勒技术学院）；Uttarakhand Open University（北阿坎德邦开放大学）
Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking pattern…
Harbin Engineering University（哈尔滨工程大学）；University of British Columbia Okanagan（不列颠哥伦比亚大学奥肯那根分校）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Institute of Technology Zhuhai（北京理工大学珠海学院）
The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences. The relations between frames, however, have not received adequate attention in comparison to the intra-frame features. In this paper, motivated by optical flow, the bilateral motion-oriented features are proposed, which can allow the classic convolutional structure to have the capability to directly portray gait movement patterns at the feature level. Based on such features, we develop a set of multi-scale temporal representations that force the motion context to be richly …
1 Shanghai Key Lab of Intelligent Information Processing, School of Computer Science 2 Institute of Science
Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods can learn gait features from gait sequences in different ways, the performance of gait recognition suffers from insufficient labeled data, especially in some practical scenarios associated with short gait sequences or various clothing styles. It is unpractical to label the numerous gait data. In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, di…
Hebei Key Laboratory for Electromagnetic Environmental Effects and Information Processing（河北省电磁环境效应与信息处理重点实验室）；Shijiazhuang Tiedao University（石家庄铁道大学）
Gait recognition, which can realize long-distance and contactless identification, is an important biometric technology. Recent gait recognition methods focus on learning the pattern of human movement or appearance during walking, and construct the corresponding spatio-temporal representations. However, different individuals have their own laws of movement patterns, simple spatial-temporal features are difficult to describe changes in motion of human parts, especially when confounding variables such as clothing and carrying are included, thus distinguishability of features is reduced. In this p…
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学技术学院）；College of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）；School of Informatics, University of Leicester（莱斯特大学信息学院）
It is a challenging task to identify a person based on her/his gait patterns. State-of-the-art approaches rely on the analysis of temporal or spatial characteristics of gait, and gait recognition is usually performed on single modality data (such as images, skeleton joint coordinates, or force signals). Evidence has shown that using multi-modality data is more conducive to gait research. Therefore, we here establish an automated learning system, with an associated spatio-temporal capsule network (ASTCapsNet) trained on multi-sensor datasets, to analyze multimodal information for gait recogniti…
DAGait: Generalized Skeleton-Guided Data Alignment for Gait Recognition
The Shenzhen International Graduate School, Tsinghua University Shenzhen, China（深圳国际研究生院，清华大学深圳学院中国）；School of Computer Science（计算机科学学院）；Information Engineering, Hefei University of Technology Hefei, China（信息工程，合肥工业大学合肥中国）
Attribute-Centric Vision-Language Alignment for Fine-Grained Gait Understanding
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Southern University of Science and Technology（南方科技大学）；Watrix Technology Limited Co. Ltd.（华瑞芯科技有限公司）
Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features. However, existing methods often rely on multi-scale operators for extracting long-range relationships among joints resulting in biased weighting. In this paper, we present HEATGait, a gait recognition system that improves the existing multi-scale graph convolution by efficient hop-extraction technique to alleviate the issue. Combined with preprocessing and augmenta…
It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment
Hangzhou Dianzi University（杭州电子科技大学）；Lishui Institute of Hangzhou Dianzi University（杭州电子科技大学丽水学院）；JD Explore Academy（京东探索研究院）；The Experimental High School Attached To Beijing Normal University（北京师范大学附属实验中学）；University of Science and Technology of China（中国科学技术大学）
CGTGait: Collaborative Graph and Transformer for Gait Emotion Recognition
Huazhong University of Science and Technology, China（华中科技大学）；Wuhan University of Technology, China（武汉理工大学）；Hefei University of Technology, China（合肥工业大学）；Boston University, United States（波士顿大学）
Gait recognition i.e. identification of an individual from his/her walking pattern is an emerging field. While existing gait recognition techniques perform satisfactorily in normal walking conditions, there performance tend to suffer drastically with variations in clothing and carrying conditions. In this work, we propose a novel covariate cognizant framework to deal with the presence of such covariates. We describe gait motion by forming a single 2D spatio-temporal template from video sequence, called Average Energy Silhouette image (AESI). Zernike moment invariants (ZMIs) are then computed t…
Gait, as one of unique biometric features, has the advantage of being recognized from a long distance away, can be widely used in public security. Considering 3D pose estimation is more challenging than 2D pose estimation in practice , we research on using 2D joints to recognize gait in this paper, and a new model-based gait recognition method JointsGait is put forward to extract gait information from 2D human body joints. Appearance-based gait recognition algorithms are prevalent before. However, appearance features suffer from external factors which can cause drastic appearance variations, e…
Pose2Gait: Extracting Gait Features from Monocular Video of Individuals with Dementia
University of Toronto（多伦多大学）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（多伦多康复研究所KITE研究所，大学健康网络）；The Hospital for Sick Children（病童医院）；Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network（多伦多西部医院莫顿与格洛丽亚·舒尔曼运动障碍诊所，大学健康网络）；Krembil Brain Institute, University Health Network（大学健康网络克雷姆比尔脑研究所）；Center for Advancing Neurotechnological Innovation to Application（神经技术创新应用推进中心）；Centre for Mental Health, University Health Network（大学健康网络心理健康中心）
Single-Stream Multi-Feature Fusion with Temporal Robustness for Gait Emotion Recognition
College of Computer and Information Science, Southwest University（西南大学计算机与信息科学学院）；College of Physical Science and Technology, Southwest University（西南大学物理科学与技术学院）；Wisesoft Inc.（四川川大智胜软件股份有限公司）
School of Cyber Science and Engineering, Shanghai Jiao Tong University（上海交通大学网络空间安全学院）
As an emerging biological identification technology, vision-based gait identification is an important research content in biometrics. Most existing gait identification methods extract features from gait videos and identify a probe sample by a query in the gallery. However, video data contains redundant information and can be easily influenced by bagging (BG) and clothing (CL). Since human body skeletons convey essential information about human gaits, a skeleton-based gait identification network is proposed in our project. First, extract skeleton sequences from the video and map them into a gai…
Harbin Engineering University（哈尔滨工程大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
As a unique and promising biometric, video-based gait recognition has broad applications. The key step of this methodology is to learn the walking pattern of individuals, which, however, often suffers challenges to extract the behavioral feature from a sequence directly. Most existing methods just focus on either the appearance or the motion pattern. To overcome these limitations, we propose a sequential convolutional network (SCN) from a novel perspective, where spatiotemporal features can be learned by a basic convolutional backbone. In SCN, behavioral information extractors (BIE) are constr…
DINOv2 Driven Gait Representation Learning for Video-Based Visible-Infrared Person Re-identification
Kunming University of Science and Technology（昆明理工大学）；Chongqing University of Post and Telecommunications（重庆邮电大学）；China University of Mining Technology（中国矿业大学）；Nanjing University of Science and Technology（南京理工大学）
Video-Language Alignment via Spatio-Temporal Graph Transformer
School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB)（北京科技大学计算机与通信工程学院）；Institute of Artificial Intelligence, University of Science and Technology Beijing (USTB)（北京科技大学人工智能研究院）；USTB-EEasyTech Joint Lab of Artificial Intelligence（北京科技大学-易智科技人工智能联合实验室）；Tencent Hunyuan（腾讯混元）；Tencent（腾讯）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）
FoleyDesigner: Immersive Stereo Foley Generation with Precise Spatio-Temporal Alignment for Film Clips
Shanghai Film Academy, Shanghai University（上海大学上海电影学院）；Shanghai Engineering Research Center of Motion Picture Special Effects（上海电影特效工程技术研究中心）；University of Surrey, UK（英国萨里大学）
Gait recognition refers to the identification of individuals based on features acquired from their body movement during walking. Despite the recent advances in gait recognition with deep learning, variations in data acquisition and appearance, namely camera angles, subject pose, occlusions, and clothing, are challenging factors that need to be considered for achieving accurate gait recognition systems. In this paper, we propose a network that first learns to extract gait convolutional energy maps (GCEM) from frame-level convolutional features. It then adopts a bidirectional recurrent neural ne…
Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；University of Cordoba（科尔多瓦大学）；The Hong Kong Polytechnic University（香港理工大学）
Nanjing University of Information Science and Technology（南京信息工程大学）；JD Finance America Corporation（京东金融美国公司）
Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To address this issue, we develop an effective spatiotemporal feature alignment network tailored to VSP, mainly including two key sub-networks: a multi-scale deformable convolutional alignment network (MDAN) and a bidirectional convolutional Long Short-Term Memory (Bi-ConvLSTM) network. The MDAN learns to align the features of the neighboring frames to the reference one in a coarse-to-fine manner, which can well handle v…
The Hong Kong University of Science and Technology（香港科技大学）；Kuaishou Technology（快手科技）
Human actions involve complex pose variations and their 2D projections can be highly ambiguous. Thus 3D spatio-temporal or 4D (i.e., 3D+T) human skeletons, which are photometric and viewpoint invariant, are an excellent alternative to 2D+T skeletons/pixels to improve action recognition accuracy. This paper proposes a new 4D dataset HAA4D which consists of more than 3,300 RGB videos in 300 human atomic action classes. HAA4D is clean, diverse, class-balanced where each class is viewpoint-balanced with the use of 4D skeletons, in which as few as one 4D skeleton per class is sufficient for trainin…
Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning temporal domain and lack of large-scale video matting datasets. In this paper, we propose a deep learning-based video matting framework which employs a novel and effective spatio-temporal feature aggregation module (ST-FAM). As optical flow estimation can be very unreliable within matting regions, ST-FAM is designed to effectively align and aggregate information across different spa…
PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment
University of Electronic Science and Technology of China（电子科技大学）；University of Würzburg（维尔茨堡大学）；Shanghai Jiao Tong University（上海交通大学）；ETH Zurich（苏黎世联邦理工学院）
Chongqing University（重庆大学）；Nanning Normal University（南宁师范大学）；University of Technology Sydney（悉尼科技大学）
Gait emotion recognition plays a crucial role in the intelligent system. Most of the existing methods recognize emotions by focusing on local actions over time. However, they ignore that the effective distances of different emotions in the time domain are different, and the local actions during walking are quite similar. Thus, emotions should be represented by global states instead of indirect local actions. To address these issues, a novel Multi Scale Adaptive Graph Convolution Network (MSA-GCN) is presented in this work through constructing dynamic temporal receptive fields and designing mul…
Representation Alignment Contrastive Regularization for Multi-Object Tracking
College of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology（浙江省大数据与未来电子商务技术重点实验室）；Zhejiang Uniview Technologies Co.,Ltd.（浙江宇视科技有限公司）
Fine-grained Context and Multi-modal Alignment for Freehand 3D Ultrasound Reconstruction
Shenzhen University（深圳大学）；School of Biomedical Engineering, Shenzhen University Medical School（深圳大学医学院生物医学工程学院）；National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（国家区域医学超声关键技术工程实验室）；Marshall Laboratory of Biomedical Engineering, Shenzhen University（深圳大学马歇尔生物医学工程实验室）
The University of Sydney（悉尼大学）；Northwestern Polytechnical University（西北工业大学）；Institute of Automation Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease, which is a neurodegenerative disorder of the central nervous system impacting millions of people around the world. To address the pressing need to improve the quality of treatment for FoG, devising a computer-aided detection and quantification tool for FoG has been increasingly important. As a non-invasive technique for collecting motion patterns, the footstep pressure sequences obtained from pressure sensitive gait mats provide a great opportunity for evaluating FoG in the clinic and potentially in the home envi…
Spatio-Temporal video grounding (STVG) focuses on retrieving the spatio-temporal tube of a specific object depicted by a free-form textual expression. Existing approaches mainly treat this complicated task as a parallel frame-grounding problem and thus suffer from two types of inconsistency drawbacks: feature alignment inconsistency and prediction inconsistency. In this paper, we present an end-to-end one-stage framework, termed Spatio-Temporal Consistency-Aware Transformer (STCAT), to alleviate these issues. Specially, we introduce a novel multi-modal template as the global objective to addre…
ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection
Jinan University（济南大学）；The First Affiliated Hospital of Jinan University（济南大学第一附属医院）；Western University（西方大学）；Guangdong University of Technology（广东工业大学）；Tsinghua University（清华大学）
ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection
MOE Key Laboratory of ICSP, IMIS Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Zenmorn-AHU AI Joint Laboratory, School of Computer Science and Technology, Anhui University（教育部智能计算与信号处理重点实验室、安徽省智能感知与机器智能重点实验室、安徽省多模态认知计算重点实验室、安徽大学智源-安徽大学人工智能联合实验室、安徽大学计算机科学与技术学院）；School of Public Safety and Emergency Management, Anhui University of Science and Technology（安徽理工大学公共安全与应急管理学院）
