Depth occlusion perception feature analysis for person re-identification

Abstract

• Occlusion perception person re-identification. • Research on deep neural network framework. • Person re-identification dataset GAN generation. Person re-identification (ReID) has achieved significant improvement under the setting of matching two holistic person images. However, persons are easily occluded by the various objects and other persons in real-world scenarios, making Person ReID a challenging task. In this paper, we propose a novel method named Pose-Driven Visibility Model (PDVM) to effectively solve the degradation of recognition performance caused by occlusion. Firstly, we extract non-occluded human body features through pose estimation, pay attention to the salient features of non-human parts through self-attention mechanism, and obtains the final feature representation after the combination. Secondly, we more accurately locate person body parts by utilizing the detected human keypoints in different occlusion situations, effectively reducing the impact of unalignment and realizing better matching for persons. We implement extensive experiments on Occluded-DukeMTMC and Partial-REID. Our proposed method achieves state of the art performances which reaches 53.0% Rank-1 accuracy on Occluded-DukeMTMC dataset and ablation analysis also verify the effectiveness of our method. 2020 Elsevier Ltd. All rights reserved