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Person Re-Identification Method Based on Keypoint-Based Dynamic Region Partitioning and APLNet Network*
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Tiangong University

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    Abstract:

    Person re-identification (Re-ID) evaluates individual consistency across surveillance cameras and is crucial in intelligent video analysis. This study introduces three innovations: (1) Keypoint-Based Dynamic Region Partitioning (KDRP), ad-dressing limitations in traditional stripe segmentation under varying angles and postures; (2) Adaptive Average Pooling Layer List Network (APLNet), enhancing feature extraction stability and computational efficiency; and (3) Priority Circle Loss (P-Circle Loss), assigning higher weights to key samples to boost model discrimination and accelerate training. Ex-periments on the filtered Market-1501 dataset achieved 85.61% mAP and 96.42% Rank-1 accuracy, showcasing superior adaptability to variations and improved Re-ID accuracy.

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  • Received:December 13,2024
  • Revised:January 10,2025
  • Adopted:February 17,2025
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