Gait Identification under Surveillance Environment based on Human Skeleton

• URL: http://arxiv.org/abs/2111.11720v2
• Date: Wed, 24 Nov 2021 14:43:51 GMT
• Title: Gait Identification under Surveillance Environment based on Human Skeleton
• Authors: Xingkai Zheng, Xirui Li, Ke Xu, Xinghao Jiang, Tanfeng Sun
• Abstract summary: A skeleton-based gait identification network is proposed in our project. First, extract skeleton sequences from the video and map them into a gait graph. Then a feature extraction network based on Spatio-Temporal Graph Convolutional Network (ST-GCN) is constructed to learn gait representations.
• Score: 18.344231394082506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 gait graph. Then a feature extraction network based on Spatio-Temporal Graph Convolutional Network (ST-GCN) is constructed to learn gait representations. Finally, the probe sample is identified by matching with the most similar piece in the gallery. We tested our method on the CASIA-B dataset. The result shows that our approach is highly adaptive and gets the advanced result in BG, CL conditions, and average.

Related papers

• AtGCN: A Graph Convolutional Network For Ataxic Gait Detection [0.0]
  This paper presents a graph convolution network called AtGCN for detecting ataxic gait. The problem is challenging as the deviation of an ataxic gait from a healthy gait is very subtle. The proposed AtGCN model outperforms the state-of-the-art in detection and prediction with an accuracy of 93.46% and a MAE of 0.4169, respectively.
  arXiv  Detail & Related papers  (2024-10-30T09:55:30Z)
• Open-Vocabulary Object Detection via Scene Graph Discovery [53.27673119360868]
  Open-vocabulary (OV) object detection has attracted increasing research attention. We propose a novel Scene-Graph-Based Discovery Network (SGDN) that exploits scene graph cues for OV detection.
  arXiv  Detail & Related papers  (2023-07-07T00:46:19Z)
• Towards a Deeper Understanding of Skeleton-based Gait Recognition [4.812321790984493]
  In recent years, most gait recognition methods used the person's silhouette to extract the gait features. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks.
  arXiv  Detail & Related papers  (2022-04-16T18:23:37Z)
• Keypoint Message Passing for Video-based Person Re-Identification [106.41022426556776]
  Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Existing methods are mostly based on convolutional neural networks (CNNs), whose building blocks either process local neighbor pixels at a time, or, when 3D convolutions are used to model temporal information, suffer from the misalignment problem caused by person movement. In this paper, we propose to overcome the limitations of normal convolutions with a human-oriented graph method. Specifically, features located at person joint keypoints are extracted and connected as a spatial-temporal graph
  arXiv  Detail & Related papers  (2021-11-16T08:01:16Z)
• Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings [0.0]
  We introduce topological descriptors of time series data to enhance hand pose classification. We observe robust results in terms of ac-curacy for a four-labels classification problem, with limited available data.
  arXiv  Detail & Related papers  (2021-10-09T22:04:43Z)
• Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
  We design a series of forgery detection systems that each focus on one individual part of the face. We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
  arXiv  Detail & Related papers  (2021-09-21T16:18:45Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition [77.77786072373942]
  This paper proposes a Transferable Neighborhood Discovery (TraND) framework to bridge the domain gap for unsupervised cross-domain gait recognition. We design an end-to-end trainable approach to automatically discover the confident neighborhoods of unlabeled samples in the latent space. Our method achieves state-of-the-art results on two public datasets, i.e., CASIA-B and OU-LP.
  arXiv  Detail & Related papers  (2021-02-09T03:07:07Z)
• GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition [6.353193172884524]
  Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. Most gait recognition methods use silhouette images to represent a person in each frame. We propose GaitGraph that combines skeleton poses with Graph Convolutional Network (GCN) to obtain a modern model-based approach for gait recognition.
  arXiv  Detail & Related papers  (2021-01-27T07:14:06Z)
• Associated Spatio-Temporal Capsule Network for Gait Recognition [36.85667679699001]
  State-of-the-art approaches rely on analysis of temporal or spatial characteristics of gait. ASTCapsNet trained on multi-sensor datasets to analyze multimodal information for gait recognition.
  arXiv  Detail & Related papers  (2021-01-07T09:55:17Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.