Multi-year long-term person re-identification using gait and HAR features

• A real-world dataset was collected from ultra-distance runners at different locations in 2020 and 2023, introducing realistic long-term Re-ID challenges like domain shift and appearance changes. • A two-stream Re-ID model combining gait and human action recognition (HAR) features through a cross-attention fusion, enriching gait-based identity cues with behavior context. • The method significantly improves over gait-only baselines, with up to 12 % mAP gain in cross-year evaluations and 11.6 % in same-year evaluations. • Cross-attention fusion allows the model to prioritize gait information while adaptively integrating activity cues from HAR, leading to faster convergence and higher Rank-1 accuracy. • Experimental results show that the fusion of motion and behavior signals outperforms traditional appearance-based Re-ID and standalone gait methods, especially in unconstrained outdoor environments. 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.