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

2026-01-09

arXiv.org

AI Summary

This work addresses the challenges of video-based person re-identification at extreme distances, where low resolution, drastic viewpoint variations, and appearance noise severely degrade performance. To tackle these issues, the authors propose a scale-adaptive framework comprising three core components: a memory-augmented visual backbone, multi-granularity temporal modeling, and prior-regularized shape dynamics. The method innovatively integrates a CLIP visual encoder, a multi-agent memory mechanism, and human body shape priors to enable adaptive modeling of structural and motion cues specific to distant pedestrians. Extensive experiments on the VReID-XFD benchmark demonstrate the effectiveness of each module, with the overall approach achieving state-of-the-art performance and securing the top position on the VReID-XFD challenge leaderboard.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Video-based Person Re-Identification
• Extreme Far Distances
• Resolution Degradation
• Viewpoint Variation
• Appearance Noise

Innovation

• Scale-Adaptive Framework
• Memory-Enhanced Visual Backbone
• Multi-Granularity Temporal Modeling
• Prior-Regularized Shape Dynamics
• Video-based Person Re-Identification