Multi-Year Long-Term Person Re-Identification Using Gait and HAR Features

Abstract

We propose a two-stream person re-identification (Re-ID) framework that integrates gait and human action recognition (HAR) through cross-attention fusion. The model processes gait sequences via a BiLSTM-based encoder to capture temporal motion dynamics. At the same time, HAR embeddings are extracted using pre-trained video backbones and distilled into compact behavioral features. These two modalities are fused using a cross-attention mechanism, enriching gait-based identity representations with context-aware activity cues. We evaluate our method on a newly curated long-term spatio-temporal dataset of ultra-distance runners captured in natural outdoor settings across multiple locations spanning three years (2020 to 2023). Experimental results demonstrate that integrating HAR significantly enhances gait-based Re-ID performance. Compared to gait-only models, our approach yields a 12% improvement in mean Average Precision (mAP) in cross-year scenarios and up to an 11.6% gain in same-year evaluations. The HAR-enhanced models also exhibit faster convergence and higher Rank-1 accuracy, establishing the effectiveness of multi-modal motion-based representations for long-term, real-world person Re-ID.