TSDW: A Tri-Stream Dynamic Weight Network for Cloth-Changing Person Re-Identification

• URL: http://arxiv.org/abs/2503.00477v1
• Date: Sat, 01 Mar 2025 13:04:49 GMT
• Title: TSDW: A Tri-Stream Dynamic Weight Network for Cloth-Changing Person Re-Identification
• Authors: Ruiqi He, Zihan Wang, Xiang Zhou,
• Abstract summary: Cloth-Changing Person Re-identification aims to solve the challenge of identifying individuals across different temporal-spatial scenarios.<n>Existing ReID research primarily relies on face recognition, semantic recognition, and clothing-irrelevant feature identification.<n>We propose a Tri-Stream Dynamic Weight Network (TSDW) that requires only images.
• Score: 10.51699935302901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cloth-Changing Person Re-identification (CC-ReID) aims to solve the challenge of identifying individuals across different temporal-spatial scenarios, viewpoints, and clothing variations. This field is gaining increasing attention in big data research and public security domains. Existing ReID research primarily relies on face recognition, gait semantic recognition, and clothing-irrelevant feature identification, which perform relatively well in scenarios with high-quality clothing change videos and images. However, these approaches depend on either single features or simple combinations of multiple features, making further performance improvements difficult. Additionally, limitations such as missing facial information, challenges in gait extraction, and inconsistent camera parameters restrict the broader application of CC-ReID. To address the above limitations, we innovatively propose a Tri-Stream Dynamic Weight Network (TSDW) that requires only images. This dynamic weighting network consists of three parallel feature streams: facial features, head-limb features, and global features. Each stream specializes in extracting its designated features, after which a gating network dynamically fuses confidence levels. The three parallel feature streams enhance recognition performance and reduce the impact of any single feature failure, thereby improving model robustness. Extensive experiments on benchmark datasets (e.g., PRCC, Celeb-reID, VC-Clothes) demonstrate that our method significantly outperforms existing state-of-the-art approaches.

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