Person Search via A Mask-Guided Two-Stream CNN Model
Di Chen, Shanshan Zhang, Wanli Ouyang, Jian Yang, Ying Tai
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In this work, we tackle the problem of person search, which is a challenging task consisted of pedestrian detection and person re-identification~(re-ID). Instead of sharing representations in a single joint model, we find that separating detector and re-ID feature extraction yields better performance. In order to extract more representative features for each identity, we segment out the foreground person from the original image patch. We propose a simple yet effective re-ID method, which models foreground person and original image patches individually, and obtains enriched representations from two separate CNN streams. From the experiments on two standard person search benchmarks of CUHK-SYSU and PRW, we achieve mAP of 83.0\% and 32.6\% respectively, surpassing the state of the art by a large margin (more than 5pp).