Multi-view Information Integration and Propagation for Occluded Person Re-identification
This addresses the problem of identifying occluded pedestrians in surveillance for security applications, offering a novel multi-view approach that is incremental over single-image methods.
The paper tackles occluded person re-identification by integrating multi-view images to create a comprehensive representation, achieving state-of-the-art results with top-1 accuracy of 85.7% on Occluded-DukeMTMC and 78.9% on Occluded-REID.
Occluded person re-identification (re-ID) presents a challenging task due to occlusion perturbations. Although great efforts have been made to prevent the model from being disturbed by occlusion noise, most current solutions only capture information from a single image, disregarding the rich complementary information available in multiple images depicting the same pedestrian. In this paper, we propose a novel framework called Multi-view Information Integration and Propagation (MVI$^{2}$P). Specifically, realizing the potential of multi-view images in effectively characterizing the occluded target pedestrian, we integrate feature maps of which to create a comprehensive representation. During this process, to avoid introducing occlusion noise, we develop a CAMs-aware Localization module that selectively integrates information contributing to the identification. Additionally, considering the divergence in the discriminative nature of different images, we design a probability-aware Quantification module to emphatically integrate highly reliable information. Moreover, as multiple images with the same identity are not accessible in the testing stage, we devise an Information Propagation (IP) mechanism to distill knowledge from the comprehensive representation to that of a single occluded image. Extensive experiments and analyses have unequivocally demonstrated the effectiveness and superiority of the proposed MVI$^{2}$P. The code will be released at \url{https://github.com/nengdong96/MVIIP}.