AlignedReID++: Dynamically matching local information for person re-identification. (October 2019)

• Record Type: Journal Article
• Authors: Luo, Hao; Jiang, Wei; Zhang, Xuan; Fan, Xing; Qian, Jingjing; Zhang, Chi
• Is Part Of: Pattern recognition. Volume 94(2019:Oct.)
• Page Start: 53
• Page End: 61
• Publication Date: 2019-10
• Subjects: Person re-identification -- CNNs -- Dynamically alignment; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.05.028
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We porpose a new method name DMLI that can dynamically match horizontal stripes without requiring extra supervision or explicit pose estimation. We introduce a local branch based on DMLI and design a novel framework called AlignedReID++, which can guide the global branch to learn more discriminative global features. Experimental results demonstrate that the proposed approach achieves competitive results in both rank-1 accuracy and mAP on Market1501, DukeMTMCReID, CUHK03 and MSMT17 databases. Abstract: Person re-identification (ReID) is a challenging problem, where global features of person images are not enough to solve unaligned image pairs. Many previous works used human pose information to acquire aligned local features to boost the performance. However, those methods need extra labeled data to train an available human pose estimation model. In this paper, we propose a novel method named Dynamically Matching Local Information (DMLI) that could dynamically align local information without requiring extra supervision. DMLI could achieve better performance, especially when encountering the human pose misalignment caused by inaccurate person detection boxes. Then, we propose a deep model name AlignedReID++ which is jointly learned with global features and local feature based on DMLI. AlignedReID++ improves the performance of global features, and could use DMLI to further increase accuracy in the inference phase. Experiments show effectiveness of our proposed method in comparison with several state-of-the-art person ReID approaches. Additionally, it achieves rank-1 accuracy of 92.8% on Market1501 and 86.2% on DukeMTMCReID with ResNet50. The code and models have been released 2 .