Pedestrian Re-Identification Based on Weakly Supervised Multi-Feature Fusion

This article proposes a weakly supervised multi-feature fusion pedestrian re-identification method, which introduces a multi-feature fusion mechanism to extract feature information from different layers into the same feature space and fuse them into the deep and shallow joint features. The goal is to fully utilize the rich information in the image and improve the performance and robustness of the pedestrian re-identification model. Secondly, by matching the target character with unprocessed surveillance videos, one only needs to know that the identity of a person appears in the video, without annotating the identity of a person in any of the frames of the video during the training process. This simplifies the annotation of training images by replacing accurate annotations with broad annotations; that is, it puts the pedestrian identities that appeared in the video in one package and assigns a video-level label to each package. This greatly reduces the annotation work and transforms this weakly supervised pedestrian re-identification challenge into a multi-instance and multi-label learning problem. The experimental results show that the method proposed in this paper is effective and can significantly improve mAP.