Multi-granular inter-frame relation exploration and global residual embedding for video-based person re-identification

Abstract

In recent years, the field of video-based person re-identification (re-ID) has conducted in-depth research on how to effectively utilize spatiotemporal clues, which has attracted attention for its potential in providing comprehensive view representations of pedestrians. However, although the discriminability and correlation of spatiotemporal features are often studied, the exploration of the complex relationships between these features has been relatively neglected. Especially when dealing with multi-granularity features, how to depict the different spatial representations of the same person under different perspectives becomes a challenge. To address this challenge, this paper proposes a multi-granularity inter-frame relationship exploration and global residual embedding network specifically designed to solve the above problems. This method successfully extracts more comprehensive and discriminative feature representations by deeply exploring the interactions and global differences between multi-granularity features. Specifically, by simulating the dynamic relationship of different granularity features in long video sequences and using a structured perceptual adjacency matrix to synthesize spatiotemporal information, cross-granularity information is effectively integrated into individual features. In addition, by introducing a residual learning mechanism, this method can also guide the diversified development of global features and reduce the negative impacts caused by factors such as occlusion. Experimental results verify the effectiveness of this method on three mainstream benchmark datasets, significantly surpassing state-of-the-art solutions. This shows that this paper successfully solves the challenging problem of how to accurately identify and utilize the complex relationships between multi-granularity spatiotemporal features in video-based person re-ID.