Gait and movement analysis have become a well-established clinical tool for diagnosing health conditions, monitoring disease progression for a wide spectrum of diseases, and to implement and assess treatment, surgery and or rehabilitation interventions. However, quantitative motion assessment remains limited to costly motion capture systems and specialized personnel, restricting its accessibility and broader application. Recent advancements in deep neural networks have enabled quantitative movement analysis using single-camera videos, offering an accessible alternative to conventional motion capture systems. In this paper, we present an efficient approach for clinical gait analysis through a dual-pattern input convolutional Transformer network. The proposed system leverages a dual-input Transformer model to estimate essential gait parameters from single RGB videos captured by a single-view camera. The system demonstrates high accuracy in estimating critical metrics such as the gait deviation index (GDI), knee flexion angle, step length, and walking cadence, validated on a dataset of individuals with movement disorders. Notably, our approach surpasses state-of-the-art methods in various scenarios, using fewer resources and proving highly suitable for clinical application, particularly in resource-constrained environments.
Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their advantages and drawbacks. In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis. The GaitPattern c…
Southeast University（东南大学）；University of Minnesota（明尼苏达大学）；Singapore University of Technology and Design（新加坡科技设计大学）；The University of Edinburgh（爱丁堡大学）
Human identification is a key requirement for many applications in everyday life, such as personalized services, automatic surveillance, continuous authentication, and contact tracing during pandemics, etc. This work studies the problem of cross-modal human re-identification (ReID), in response to the regular human movements across camera-allowed regions (e.g., streets) and camera-restricted regions (e.g., offices) deployed with heterogeneous sensors. By leveraging the emerging low-cost RGB-D cameras and mmWave radars, we propose the first-of-its-kind vision-RF system for cross-modal multi-per…
University of Oxford（牛津大学）；University of Glasgow（格拉斯哥大学）；Shanghai Jiao Tong University（上海交通大学）
With the increasing awareness of high-quality life, there is a growing need for health monitoring devices running robust algorithms in home environment. Health monitoring technologies enable real-time analysis of users' health status, offering long-term healthcare support and reducing hospitalization time. The purpose of this work is twofold, the software focuses on the analysis of gait, which is widely adopted for joint correction and assessing any lower limb or spinal problem. On the hardware side, we design a novel marker-less gait analysis device using a low-cost RGB camera mounted on a mo…
Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette images to represent a person in each frame. However, silhouette images can lose fine-grained spatial information, and most papers do not regard how to obtain these silhouettes in complex scenes. Furthermore, silhouette images contain not only gait features but also other visual clues that can be recognized. Hence these approaches can not be considered as strict gait recognition. We leverage recent advances in human pose…
Zhejiang University（浙江大学）；Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province（浙江省协同感知与自主无人系统重点实验室）
Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveillance may cause privacy invasion. Due to the robustness and non-invasive feature of millimeter wave (mmWave) radar, radar-based gait recognition has attracted increasing attention in recent years. In this research, we propose a Hierarchical Dynamic Network (HDNet) for gait recogni…
Southern University of Science and Technology（南方科技大学）；Beijing Normal University（北京师范大学）；The University of Hong Kong（香港大学）
Gait is one of the most promising biometrics to identify individuals at a long distance. Although most previous methods have focused on recognizing the silhouettes, several end-to-end methods that extract gait features directly from RGB images perform better. However, we demonstrate that these end-to-end methods may inevitably suffer from the gait-irrelevant noises, i.e., low-level texture and colorful information. Experimentally, we design the cross-domain evaluation to support this view. In this work, we propose a novel end-to-end framework named GaitEdge which can effectively block gait-irr…
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Yale University（耶鲁大学）
As mobile technologies have become ubiquitous in recent years, computer-based cognitive tests have become more popular and efficient. In this work, we focus on assessing motor function in children by analyzing their gait movements. Although there has been a lot of research on designing automated assessment systems for gait analysis, most of these efforts use obtrusive wearable sensors for measuring body movements. We have devised a computer vision-based assessment system that only requires a camera which makes it easier to employ in school or home environments. A dataset has been created with …
A non-invasive video-based method for individual identification of wildlife using gait dynamics
Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Wildlife Conservation Research Unit, Department of Biology, University of Oxford（牛津大学生物系野生动物保护研究组）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；PediaMed AI；Shenzhen Children’s Hospital（深圳市儿童医院）；Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Newcastle University（纽卡斯尔大学）；University of Cambridge（剑桥大学）；Durham University（杜伦大学）；Georgia Institute of Technology（佐治亚理工学院）
Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades. Because of its nature as a long-distance biometric trait, gait can be easily collected and used to identify individuals non-intrusively through CCTV cameras. However, it is very difficult to develop robust automated gait recognition systems, since gait may be affected by many covariate factors such as clothing, walking speed, camera view angle etc. Out of them, large view angle changes has been deemed as the most challeng…
CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson's Disease Gait Assessment
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；KITE Research Institute-UHN（KITE研究 institute-UHN）；University of Strasbourg（斯特拉斯堡大学）；University Hospitals of Strasbourg（斯特拉斯堡大学医院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Federal University of ABC（巴西ABC联邦大学）；KU Leuven（鲁文大学）；Hasselt University（哈瑟尔特大学）；Emory University（埃默里大学）；University of Bristol（布里斯托尔大学）
In this work, we present a novel framework for on-line human gait stability prediction of the elderly users of an intelligent robotic rollator using Long Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range Finder (LRF) data from non-wearable sensors. A Deep Learning (DL) based approach is used for the upper body pose estimation. The detected pose is used for estimating the body Center of Mass (CoM) using Unscented Kalman Filter (UKF). An Augmented Gait State Estimation framework exploits the LRF data to estimate the legs' positions and the respective gait phase. These es…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Maryland at College Park（马里兰大学帕克分校）
We present a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking, we extract his/her walking gait in the form of a series of 3D poses. Our goal is to exploit the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. Our perceived emotion recognition approach uses deep features learned via LSTM on labeled emotion datasets. Furthermore, we combine these features with affective features computed from gaits using posture and mov…
Department of Computer Science and Engineering, Southern University of Science and Technology, China（南方科技大学计算机科学与工程系）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China（大数据系统计算技术国家工程实验室，深圳大学）；Shenzhen Polytechnic University, China（深圳职业技术大学）；Zhejiang University, China（浙江大学）；Alibaba Group, China（阿里巴巴集团）
Shirley Ryan AbilityLab（雪莉·瑞安能力实验室）；Northwestern University（西北大学）；Stanford University（斯坦福大学）
Markerless pose estimation allows reconstructing human movement from multiple synchronized and calibrated views, and has the potential to make movement analysis easy and quick, including gait analysis. This could enable much more frequent and quantitative characterization of gait impairments, allowing better monitoring of outcomes and responses to interventions. However, the impact of different keypoint detectors and reconstruction algorithms on markerless pose estimation accuracy has not been thoroughly evaluated. We tested these algorithmic choices on data acquired from a multicamera system …
This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects. To achieve this, we formulate a dynamic message propagation (DMP) module with the graph neural networks and deformable convolutions to dynamically learn the context information and to automatically predict filter weights and affinity matrices for message propagation control. We furt…
Peking University（北京大学）；Dalian University of Technology（大连理工大学）
Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second, we present a simple and effective way to detect and remove the offset. The resulting images, named ORGB, have almost the same appearance as the original RGB images while are more illumination-ro…
We tackle the challenging task of estimating global 3D joint locations for both hands via only monocular RGB input images. We propose a novel multi-stage convolutional neural network based pipeline that accurately segments and locates the hands despite occlusion between two hands and complex background noise and estimates the 2D and 3D canonical joint locations without any depth information. Global joint locations with respect to the camera origin are computed using the hand pose estimations and the actual length of the key bone with a novel projection algorithm. To train the CNNs for this new…
Washington University in St. Louis（圣路易斯华盛顿大学）；Adobe Research（Adobe研究院）；Argo AI（阿尔戈人工智能公司）；Simon Fraser University（西蒙弗雷泽大学）
This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image depth prediction, piece-wise planar depthmap reconstruction requires a structured geometry representation, and has been a difficult task to master even for DNNs. The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image. We have generated more than 50,000 piece-wise planar depthmaps for training and testing from ScanNet, a large-sc…
We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video. TexMesh enables high quality free-viewpoint rendering of humans. Given the RGB frames, the captured environment map, and the coarse per-frame human mesh from RGB-D tracking, our method reconstructs spatiotemporally consistent and detailed per-frame meshes along with a high-resolution albedo texture. By using the incident illumination we are able to accurately estimate local surface geometry and albedo, which allows us to further use photometric constraints to adapt…
City University of Hong Kong（香港城市大学）；Huaqiao University（华侨大学）
This paper investigates the problem of reconstructing hyperspectral (HS) images from single RGB images captured by commercial cameras, \textbf{without} using paired HS and RGB images during training. To tackle this challenge, we propose a new lightweight and end-to-end learning-based framework. Specifically, on the basis of the intrinsic imaging degradation model of RGB images from HS images, we progressively spread the differences between input RGB images and re-projected RGB images from recovered HS images via effective unsupervised camera spectral response function estimation. To enable the…
A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University（南洋理工大学）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Bonn（波恩大学）
Hand motion capture has been an active research topic in recent years, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit the practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approa…
Human motion characteristics are used to monitor the progression of neurological diseases and mood disorders. Since perceptions of emotions are also interleaved with body posture and movements, emotion recognition from human gait can be used to quantitatively monitor mood changes. Many existing solutions often use shallow machine learning models with raw positional data or manually extracted features to achieve this. However, gait is composed of many highly expressive characteristics that can be used to identify human subjects, and most solutions fail to address this, disregarding the subject'…
Beijing Jiaotong University（北京交通大学）；Meituan（美团）；City University of Hong Kong（香港城市大学）
The popularity and promotion of depth maps have brought new vigor and vitality into salient object detection (SOD), and a mass of RGB-D SOD algorithms have been proposed, mainly concentrating on how to better integrate cross-modality features from RGB image and depth map. For the cross-modality interaction in feature encoder, existing methods either indiscriminately treat RGB and depth modalities, or only habitually utilize depth cues as auxiliary information of the RGB branch. Different from them, we reconsider the status of two modalities and propose a novel Cross-modality Discrepant Interac…
MPI Informatics（马克斯·普朗克信息学研究所）；Technical University of Munich（慕尼黑工业大学）；Universidad Rey Juan Carlos（胡安·卡洛斯国王大学）
Tracking and reconstructing the 3D pose and geometry of two hands in interaction is a challenging problem that has a high relevance for several human-computer interaction applications, including AR/VR, robotics, or sign language recognition. Existing works are either limited to simpler tracking settings (e.g., considering only a single hand or two spatially separated hands), or rely on less ubiquitous sensors, such as depth cameras. In contrast, in this work we present the first real-time method for motion capture of skeletal pose and 3D surface geometry of hands from a single RGB camera that …
TECNALIA（特克纳利亚研究与创新中心）；CVC - Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）
Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can …
Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）
Our work aims to obtain 3D reconstruction of hands and manipulated objects from monocular videos. Reconstructing hand-object manipulations holds a great potential for robotics and learning from human demonstrations. The supervised learning approach to this problem, however, requires 3D supervision and remains limited to constrained laboratory settings and simulators for which 3D ground truth is available. In this paper we first propose a learning-free fitting approach for hand-object reconstruction which can seamlessly handle two-hand object interactions. Our method relies on cues obtained wit…
Dalian University of Technology（大连理工大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong (ShenZhen)（香港中文大学（深圳））；Princeton University（普林斯顿大学）
We present a deep reinforcement learning method of progressive view inpainting for colored semantic point cloud scene completion under volume guidance, achieving high-quality scene reconstruction from only a single RGB-D image with severe occlusion. Our approach is end-to-end, consisting of three modules: 3D scene volume reconstruction, 2D RGB-D and segmentation image inpainting, and multi-view selection for completion. Given a single RGB-D image, our method first predicts its semantic segmentation map and goes through the 3D volume branch to obtain a volumetric scene reconstruction as a guide…
RainDancer: RGB-Event Video Deraining with Rain-Oriented Spiking Dynamics
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Computer Science and Technology, Harbin University of Science and Technology（哈尔滨理工大学计算机科学与技术学院）
Vision-Learning-Mining Lab, University of Texas at Arlington（德克萨斯大学阿灵顿分校视觉-学习-挖掘实验室）；Heracleia - Human Centered Computing Lab, University of Texas at Arlington（德克萨斯大学阿灵顿分校Heracleia - 以人为本计算实验室）
Hand keypoints detection and pose estimation has numerous applications in computer vision, but it is still an unsolved problem in many aspects. An application of hand keypoints detection is in performing cognitive assessments of a subject by observing the performance of that subject in physical tasks involving rapid finger motion. As a part of this work, we introduce a novel hand key-points benchmark dataset that consists of hand gestures recorded specifically for cognitive behavior monitoring. We explore the state of the art methods in hand keypoint detection and we provide quantitative evalu…
Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or…
Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort
Department of Biomedical Informatics, Emory University（埃默里大学生物医学信息学系）；Shriners Children’s（夏皮罗儿童医院）；The Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology（埃默里大学和佐治亚理工学院的沃克·H·库勒生物医学工程系）
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
Beijing Jiaotong University（北京交通大学）；Shandong University（山东大学）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Southern University of Science and Technology（南方科技大学）；City University of Hong Kong（香港城市大学）
University of Chinese Academy of Sciences（中国科学院大学）；Beijing Jiaotong University（北京交通大学）；City University of Hong Kong（香港城市大学）
There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data; (2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the po…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Aalto University（阿尔托大学）
Real-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing. However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately. Many previous approaches resort to complex multi-camera setups to remedy the occlusion problem and often employ expensive segmentation and optimization steps which makes real-time tracking impossible. In this paper, we propose …
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；School of Informatics, Xiamen University（厦门大学信息学院）；Lingnan University（岭南大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科学、技术与研究局）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；School of Data and Computer Science, Sun Yat-Sen University（中山大学数据与计算机科学学院）
Motif Guided Graph Transformer with Combinatorial Skeleton Prototype Learning for Skeleton-Based Person Re-Identification
College of Computing and Data Science, Nanyang Technological University (NTU)（南洋理工大学计算与数据科学学院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)（南洋理工大学-不列颠哥伦比亚大学老年活跃生活卓越联合研究中心）
South China University of Technology（华南理工大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Microsoft Research Asia（微软亚洲研究院）
This paper focuses on the challenging task of learning 3D object surface reconstructions from single RGB images. Existing methods achieve varying degrees of success by using different geometric representations. However, they all have their own drawbacks, and cannot well reconstruct those surfaces of complex topologies. To this end, we propose in this paper a skeleton-bridged, stage-wise learning approach to address the challenge. Our use of skeleton is due to its nice property of topology preservation, while being of lower complexity to learn. To learn skeleton from an input image, we design a…
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）；Xmov（魔珐科技）；Zhejiang University（浙江大学）；Texas A&M University（德州农工大学）
We introduce an approach that accurately reconstructs 3D human poses and detailed 3D full-body geometric models from single images in realtime. The key idea of our approach is a novel end-to-end multi-task deep learning framework that uses single images to predict five outputs simultaneously: foreground segmentation mask, 2D joints positions, semantic body partitions, 3D part orientations and uv coordinates (uv map). The multi-task network architecture not only generates more visual cues for reconstruction, but also makes each individual prediction more accurate. The CNN regressor is further c…
Advances in deep learning techniques have allowed recent work to reconstruct the shape of a single object given only one RBG image as input. Building on common encoder-decoder architectures for this task, we propose three extensions: (1) ray-traced skip connections that propagate local 2D information to the output 3D volume in a physically correct manner; (2) a hybrid 3D volume representation that enables building translation equivariant models, while at the same time encoding fine object details without an excessive memory footprint; (3) a reconstruction loss tailored to capture overall objec…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland University（萨尔大学）；Universidad Rey Juan Carlos（胡安卡洛斯国王大学）
We present the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera. Our method combines a new convolutional neural network (CNN) based pose regressor with kinematic skeleton fitting. Our novel fully-convolutional pose formulation regresses 2D and 3D joint positions jointly in real time and does not require tightly cropped input frames. A real-time kinematic skeleton fitting method uses the CNN output to yield temporally stable 3D global pose reconstructions on the basis of a coherent kinematic skeleto…
