Frame Extraction Person Retrieval Framework Based on Improved YOLOv8s and the Stage-Wise Clustering Person Re-Identification

Abstract

Person re-identification (Re-ID), a crucial research area in smart city security, faces challenges due to person posture changes, object occlusion and other factors, making it difficult for existing methods to accurately retrieving target person in video surveillance. To resolve this problem, we propose a person retrieval framework that integrates YOLOv8s and person Re-ID. Improved YOLOv8s is employed to extract person categories from the video on a frame-by-frame basis, and when combined with the stage-wise clustering person Re-ID network (SCPN), it enables collaborative person retrieval across multiple cameras. Notably, a feature precision (FP) module is added in the YOLOv8s network to form FP-YOLOv8s, and SCPN incorporates innovative enhancements including the stage-wise learning rate scheduler, centralized clustering loss and adaptive representation joint attention module into the person Re-ID baseline model. Comprehensive experiments on COCO, Market-1501 and DukeMTMC-ReID datasets demonstrate that our proposed framework outperforms several other leading methods. Given the scarcity of image-video person Re-ID datasets, we also provide an extended image-video person (EIVP) dataset, which contains 102 videos and 814 bounding boxes of 57 identities captured by 8 cameras. The video reasoning detection score of this framework reaches 78.8% on this dataset, indicating a 3.2% increase compared to conventional models.