In-depth exploration of attribute information for person re-identification

Abstract

Pedestrian’s attribute information plays an important role in person re-identification (re-ID) for its complementary to pedestrian’s identity labels. However, there are few methods to utilize attribute information, which limits the development of re-ID community. In this paper, we analyze the effect of attribute information on re-ID to obtain both qualitative and quantitative results, indicating the potential for in-depth exploration of attribute information. On this basis, we propose an Identity Recognition Network (IRN) and an Attribute Recognition Network (ARN). IRN enhances the attention to pedestrian’s local information while identifying pedestrians’ identity. ARN calculates the attribute similarity among pedestrians accurately to promote the identification of IRN. The combination of them makes deep exploration of attribute information and is easy to implement. The experimental results on two large-scale re-ID benchmarks demonstrate the effectiveness of our method, which is on par with the state-of-the-art. In the DukeMTMC-reID dataset, mAP (rank-1) accuracy is improved from 58.4 (78.3) % to 66.4 (82.7) % for ResNet-50. In the Market1501 dataset, mAP (rank-1) accuracy is improved from 75.8 (90.5) % to 79.5 (92.8) % for ResNet-50.