Person Re-identification Method Based on Video Spatial Feature Enhancement

Recently, video-based person re-identification has received more and more attention, and has played a very important role in public safety fields such as security monitoring and public security criminal investigation. However, it is still a great challenge for existing work to effectively overcome t...

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Bibliographic Details
Published in2024 IEEE International Conference on Cognitive Computing and Complex Data (ICCD) pp. 23 - 30
Main Authors Ke, Zunwang, Sun, Guozhi, Guo, Run, Du, Minghua, Zhang, Yugui
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.09.2024
Subjects
attention
Data mining
feature enhancement
Feature extraction
Identification of persons
Monitoring
Pedestrian occlusion
Pedestrians
Person re-identification
Public security
Security
Source coding
spatial features
Splicing
Time-domain analysis
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Summary:Recently, video-based person re-identification has received more and more attention, and has played a very important role in public safety fields such as security monitoring and public security criminal investigation. However, it is still a great challenge for existing work to effectively overcome the occlusion problem and extract more abundant pedestrian spatial feature information. To tackle the problem of occlusion of pedestrians in real scenes, this paper proposes a person re-identification method based on video spatial feature enhancement. This method enhances the spatial information in video frames by using multi-angle feature aggregation of time attention and extracts continuous pedestrian detail features by using multi-frame spatial feature splicing of time domain information, so as to solve the difficult problem of pedestrian occlusion recognition. Experiments show that the proposed method has improved performance on MARS and DukeMTMC-VideoReID datasets, which verifies the effectiveness of the proposed method. The source code is available at https://github.com/sgzhi11/Video-Spatial-Feature-Enhancement.
DOI:10.1109/ICCD62811.2024.10843613