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Video-Based Person Re-Identification With Unregulated Sequences
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Author(s): Wenjun Huang (National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China), Chao Liang (National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China), Chunxia Xiao (School of Computer Science, Wuhan University, Wuhan, China)and Zhen Han (School of Computer Science, Wuhan University, Wuhan, China)
Copyright: 2020
Volume: 12
Issue: 2
Pages: 18
Source title:
International Journal of Digital Crime and Forensics (IJDCF)
Editor(s)-in-Chief: Feng Liu (Chinese Academy of Sciences, China)
DOI: 10.4018/IJDCF.2020040104
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Abstract
Video-based person re-identification (re-id) has recently attracted widespread attentions because extra space-time information and more appearance cues in videos can be used to improve the performance of image-based person re-id. Most existing approaches equally treat person video images, ignoring their individual discrepancy. However, in real scenarios, captured images are usually contaminated by various noises, especially occlusions, resulting in a series of unregulated sequences. Through investigating the impact of unregulated sequences to feature representation of video-based person re-id, the authors find a remarkable promotion by eliminating noisy sub sequences. Based on this interesting finding, an adaptive unregulated sub sequence detection and refinement method is proposed to purify original video sequence and obtain a more effective and discriminative feature representation for video-based person re-id. Experimental results on two public datasets demonstrate that the proposed method outperforms the state-of-the-art work.
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