ISBN: 978-981-18-3959-7 DOI: 10.18178/wcse.2022.06.028
Pose-Guided Multi-Granularity Feature Learning for Occluded Person Re-Identification
Abstract—Persons are often occluded in real-world applications of person re-identification. To alleviate the occlusion problem, this paper proposes a pose guided multi-granularity feature learning method for occluded person re-identification. At first, the residual atrous spatial pyramid pooling module is used to expand the receptive field to extract more hierarchical pedestrian features. Next, the visible head-and-shoulder region feature and the underneath region feature from the heatmap extracted by the pedestrian estimation algorithm are calculated. Finally, the multi-granularity strategy is adopted to learn the pedestrian features of different hierarchies of information in the visible pedestrian region. Experimental results on the Occluded- DukeMTMC, Occluded-REID, and Market1501 datasets demonstrate the effectiveness of our proposed method.
Index Terms—occluded person rei-dentification, pose estimation, multi-granularity, residual atrous spatial pyramid pooling.
Jianhua Shu, Jingsheng Lei
College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 201306, CHINA
Cite:Jianhua Shu, Jingsheng Lei, "Pose-Guided Multi-Granularity Feature Learning for Occluded Person Re-Identification, " Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering (WCSE 2022), pp. 198-206, June 24-27, 2022.