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<p>The person search task aims to address both person detection and person re-identification(re-id) simultaneously, integrating these two tasks into a unified objective. Person search is commonly used in surveillance and security fields. Currently, person search tasks in surveillance scenarios face many severe challenges, such as scale variations and occlusion issues caused by cameras. Existing approaches often overlook the discrepancies between multi-scale features and typically perform direct feature fusion. Most methods addressing occlusion rely on feature completion techniques, without fully utilizing the inherent fine-grained information from the original images. This paper proposes a Multi-level Supervised and Fine-grained Feature Enhancement for Person Search (MFPS) to mitigate these issues. MFPS employs cascaded encoders and decoders to extract person detection features from the backbone network. To generate re-id features robust to scale variations, MFPS introduces a Multi-Level Supervision method (MLS), which aggregates features of different scales and levels, enriching the semantic information of person features. Furthermore, to address the issue of missing re-id features caused by occlusion, this paper proposes a deformable fine-grained attention module. This module extracts fine-grained re-id features with accurate semantic information through sampling point offset operations. Finally, fine-grained features and multi-scale features are fused, and the re-id features extracted through multi-level supervised fine-grained feature extraction significantly improve recognition accuracy for person search tasks in surveillance scenarios. The experimental results show that MFPS improves the mAP metrics by 0.8 and the top-1 metrics by 1.8 compared to the state-of-the-art method on the PRW dataset, proving its superiority in complex environments. The source code is available at <a href="https://github.com/FengHua0208/MFPS.">https://github.com/FengHua0208/MFPS.</a></p>

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Multi-level supervised and fine-grained feature enhancement for person search

  • 期刊论文

  • 发表日期：

    2026年1月24日

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  • Xuyang Zhang,
  • Sijie Yang,
  • Hongkun Liu,
  • Heyan Jin,
  • Guangqiang Yin,
  • Ye Li

摘要

The person search task aims to address both person detection and person re-identification(re-id) simultaneously, integrating these two tasks into a unified objective. Person search is commonly used in surveillance and security fields. Currently, person search tasks in surveillance scenarios face many severe challenges, such as scale variations and occlusion issues caused by cameras. Existing approaches often overlook the discrepancies between multi-scale features and typically perform direct feature fusion. Most methods addressing occlusion rely on feature completion techniques, without fully utilizing the inherent fine-grained information from the original images. This paper proposes a Multi-level Supervised and Fine-grained Feature Enhancement for Person Search (MFPS) to mitigate these issues. MFPS employs cascaded encoders and decoders to extract person detection features from the backbone network. To generate re-id features robust to scale variations, MFPS introduces a Multi-Level Supervision method (MLS), which aggregates features of different scales and levels, enriching the semantic information of person features. Furthermore, to address the issue of missing re-id features caused by occlusion, this paper proposes a deformable fine-grained attention module. This module extracts fine-grained re-id features with accurate semantic information through sampling point offset operations. Finally, fine-grained features and multi-scale features are fused, and the re-id features extracted through multi-level supervised fine-grained feature extraction significantly improve recognition accuracy for person search tasks in surveillance scenarios. The experimental results show that MFPS improves the mAP metrics by 0.8 and the top-1 metrics by 1.8 compared to the state-of-the-art method on the PRW dataset, proving its superiority in complex environments. The source code is available at https://github.com/FengHua0208/MFPS.