AlignedReID++: Dynamically matching local information for person re-identification

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 released2