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<p>Person re-identification (re-ID) in video sequences is a central task in surveillance and computer vision, yet it continues to present substantial challenges due to occlusion, viewpoint variation, and noisy frames. This study proposes a compact deep learning framework that integrates convolutional features, recurrent temporal modeling, and multi-level similarity aggregation to effectively capture both fine-grained spatial cues and long-range temporal patterns. The framework is deliberately designed as a compact CNN–GRU architecture, thereby avoiding the depth and computational demands of transformer-based backbones while preserving robust recognition capabilities. Experimental evaluations reveal clear advantages over conventional and Siamese-based approaches, confirming the complementary nature of spatial and temporal features and the effectiveness of efficient pooling strategies. These findings indicate that accurate and resource-efficient person re-ID can be achieved through compact architectures, offering practical potential for implementation in real-world, resource-constrained environments.</p>

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A CNN-RNN Siamese framework with multi-level aggregation for video-based person re-identification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月11日

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  • Yuan-Kai Wang,
  • Tung-Ming Pan,
  • Chung-Pin Sun

摘要

Person re-identification (re-ID) in video sequences is a central task in surveillance and computer vision, yet it continues to present substantial challenges due to occlusion, viewpoint variation, and noisy frames. This study proposes a compact deep learning framework that integrates convolutional features, recurrent temporal modeling, and multi-level similarity aggregation to effectively capture both fine-grained spatial cues and long-range temporal patterns. The framework is deliberately designed as a compact CNN–GRU architecture, thereby avoiding the depth and computational demands of transformer-based backbones while preserving robust recognition capabilities. Experimental evaluations reveal clear advantages over conventional and Siamese-based approaches, confirming the complementary nature of spatial and temporal features and the effectiveness of efficient pooling strategies. These findings indicate that accurate and resource-efficient person re-ID can be achieved through compact architectures, offering practical potential for implementation in real-world, resource-constrained environments.