Synthetic-To-Real Video Person Re-ID

• URL: http://arxiv.org/abs/2402.02108v3
• Date: Tue, 04 Feb 2025 06:00:12 GMT
• Title: Synthetic-To-Real Video Person Re-ID
• Authors: Xiangqun Zhang, Wei Feng, Ruize Han, Likai Wang, Linqi Song, Junhui Hou,
• Abstract summary: Person re-identification (Re-ID) is an important task and has significant applications for public security and information forensics. We investigate a novel and challenging setting of Re-ID, i.e., cross-domain video-based person Re-ID. We utilize synthetic video datasets as the source domain for training and real-world videos for testing.
• Score: 57.937189569211505
• License:
• Abstract: Person re-identification (Re-ID) is an important task and has significant applications for public security and information forensics, which has progressed rapidly with the development of deep learning. In this work, we investigate a novel and challenging setting of Re-ID, i.e., cross-domain video-based person Re-ID. Specifically, we utilize synthetic video datasets as the source domain for training and real-world videos for testing, notably reducing the reliance on expensive real data acquisition and annotation. To harness the potential of synthetic data, we first propose a self-supervised domain-invariant feature learning strategy for both static and dynamic (temporal) features. Additionally, to enhance person identification accuracy in the target domain, we propose a mean-teacher scheme incorporating a self-supervised ID consistency loss. Experimental results across five real datasets validate the rationale behind cross-synthetic-real domain adaptation and demonstrate the efficacy of our method. Notably, the discovery that synthetic data outperforms real data in the cross-domain scenario is a surprising outcome. The code and data are publicly available at https://github.com/XiangqunZhang/UDA_Video_ReID.

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