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

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.