Focusing on pedestrians like human for clothes changing person re-identification
Wenjie Pan, Jianqing Zhu, Xiaolin Cui, Huanqiang Zeng, Yibing Zhan · Neural Networks · 2025
• Based on the ensemble coding hypothesis in cognitive neuroscience, we achieve data augmentation by simulating human focus capability. • Our method is the first local detail learning data augmentation designed specifically for clothes changing person re-identification. • Our method achieves SOTA on three public datasets and outperforms various traditional data augmentation methods. Current approaches focus mainly on the design of networks to learn key identity features from local body components for clothes-changing person re-identification (CC-ReID). In this paper, we propose a humanoid focus-inspired image augmentation (HFIA) method, which is intuitive image processing rather than a sophisticated network architecture designed to enhance local nuances of pedestrian images. Based on pedestrian silhouettes, we roughly divide a pedestrian image into five body components, that is, head-shoulder, upper left torso, upper right torso, lower left torso, and lower right torso. The HFIA has two key designs to deal with these components: the central emphasis strategy (CES) and the component continuity processing (CCP). For each component, leveraging the natural tendency of human visual attention towards central regions, the CES constructs an enlargement grid, where the closer the center, the greater the enlargement. To maintain the continuity of assembly, the CCP performs an overall alignment of component centers, that is, all components share the same normalized vertical coordinate and the left and right torsos have mirrored horizontal coordinates. Furthermore, the CCP implements a smoothing post-processing to uniformly erase the discontinuity between the head-shoulder, upper left torso, and upper right torso. Experiments show the state-of-the-art performance of HFIA.