Laser & Optoelectronics Progress, Volume. 60, Issue 10, 1010021(2023)

Occluded Video-Based Person Re-Identification Based on Spatial-Temporal Trajectory Fusion

Xiao Yun*, Kaili Song, Xiaoguang Zhang, and Xinchao Yuan
Author Affiliations
  • School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, Jiangsu, China
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    Aiming at the problem of wide-range occlusion of target pedestrians in video pedestrian re-identification, a pedestrian re-identification algorithm based on spatio-temporal trajectory fusion is proposed by combining pedestrian trajectory prediction with pedestrian re-identification, which is time-related and not affected by occlusion. First, from the time and space domains, accurate pedestrian trajectory coordinate prediction in line with social attributes is realized. Second, the spatiotemporal trajectory fusion feature is constructed to effectively combine the apparent visual features in the video sequence with the coordinate data in the pedestrian trajectory, which effectively alleviates the impact of centralized occlusion on the re-identification performance. Finally, a trajectory fusion dataset MARS_traj suitable for the proposed algorithm is constructed, and experiments show that the proposed algorithm can effectively improve the performance of the occlusion video re-identification.

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    Xiao Yun, Kaili Song, Xiaoguang Zhang, Xinchao Yuan. Occluded Video-Based Person Re-Identification Based on Spatial-Temporal Trajectory Fusion[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010021

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    Paper Information

    Category: Image Processing

    Received: Feb. 25, 2022

    Accepted: Apr. 19, 2022

    Published Online: May. 23, 2023

    The Author Email: Yun Xiao (xyun@cumt.edu.cn)

    DOI:10.3788/LOP220812

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