Person re-identification via deep compound eye network and pose repair module

Abstract

Person re-identification is aimed at searching for specific target pedestrians from non-intersecting cameras. However, in real complex scenes, pedestrians are easily obscured, which makes the target pedestrian search task time-consuming and challenging. To address the problem of pedestrians' susceptibility to occlusion, a person re-identification via deep compound eye network (CEN) and pose repair module is proposed, which includes (1) A deep CEN based on multi-camera logical topology is proposed, which adopts graph convolution and a Gated Recurrent Unit to capture the temporal and spatial information of pedestrian walking and finally carries out pedestrian global matching through the Siamese network; (2) An integrated spatial-temporal information aggregation network is designed to facilitate pose repair. The target pedestrian features under the multi-level logic topology camera are utilised as auxiliary information to repair the occluded target pedestrian image, so as to reduce the impact of pedestrian mismatch due to pose changes; (3) A joint optimisation mechanism of CEN and pose repair network is introduced, where multi-camera logical topology inference provides auxiliary information and retrieval order for the pose repair network. The authors conducted experiments on multiple datasets, including Occluded-DukeMTMC, CUHK-SYSU, PRW, SLP, and UJS-reID. The results indicate that the authors’ method achieved significant performance across these datasets. Specifically, on the CUHK-SYSU dataset, the authors’ model achieved a top-1 accuracy of 89.1% and a mean Average Precision accuracy of 83.1% in the recognition of occluded individuals.