Gait Identification under Surveillance Environment based on Human Skeleton

TL;DR

This paper proposes a skeleton-based gait identification method using Spatio-Temporal Graph Convolutional Networks, which is more robust to clothing and bagging variations than traditional video-based approaches.

Contribution

It introduces a novel skeleton sequence extraction and gait graph mapping approach combined with ST-GCN for improved gait recognition under challenging conditions.

Findings

High accuracy in BG and CL conditions

Effective skeleton-based gait representation

Outperforms traditional video-based methods

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.