TRANSFORMER-BASED PERSON SEARCH MODEL WITH SYMMETRIC ONLINE INSTANCE MATCHING

Person search is a challenging retrieval problem which aims at matching pedestrians with the same identity over non-overlapping camera views. In this paper, we adopt Swin Transformer as the backbone network to extract discriminative features. We propose a symmetric online instance matching loss which transfers the symmetric idea from KL divergence to the online instance matching loss. The purpose is to strengthen the robustness of the person search model under the condition of limited training identities. We compared with the state-of-the-arts on two mainstream benchmarks: CUHK-SYSU and PRW. Experimental results demonstrate the effectiveness of our method. Especially, we achieve better performance on the PRW dataset with improvement of 6.5% and 3.5% at mAP and top-1 accuracy, respectively.