SAS-VPReID: A Scale-Adaptive Framework with Shape Priors for Video-based Person Re-Identification at Extreme Far Distances

• URL: http://arxiv.org/abs/2601.05535v1
• Date: Fri, 09 Jan 2026 05:22:58 GMT
• Title: SAS-VPReID: A Scale-Adaptive Framework with Shape Priors for Video-based Person Re-Identification at Extreme Far Distances
• Authors: Qiwei Yang, Pingping Zhang, Yuhao Wang, Zijing Gong,
• Abstract summary: Video-based Person Re-IDentification (VPReID) aims to retrieve the same person from videos captured by non-overlapping cameras.<n>At extreme far distances, VPReID is highly challenging due to severe resolution degradation, drastic viewpoint variation and inevitable appearance noise.<n>We propose a Scale-Adaptive framework with Shape Priors for VPReID, named SAS-VPReID.
• Score: 30.963383617202755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-based Person Re-IDentification (VPReID) aims to retrieve the same person from videos captured by non-overlapping cameras. At extreme far distances, VPReID is highly challenging due to severe resolution degradation, drastic viewpoint variation and inevitable appearance noise. To address these issues, we propose a Scale-Adaptive framework with Shape Priors for VPReID, named SAS-VPReID. The framework is built upon three complementary modules. First, we deploy a Memory-Enhanced Visual Backbone (MEVB) to extract discriminative feature representations, which leverages the CLIP vision encoder and multi-proxy memory. Second, we propose a Multi-Granularity Temporal Modeling (MGTM) to construct sequences at multiple temporal granularities and adaptively emphasize motion cues across scales. Third, we incorporate Prior-Regularized Shape Dynamics (PRSD) to capture body structure dynamics. With these modules, our framework can obtain more discriminative feature representations. Experiments on the VReID-XFD benchmark demonstrate the effectiveness of each module and our final framework ranks the first on the VReID-XFD challenge leaderboard. The source code is available at https://github.com/YangQiWei3/SAS-VPReID.

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