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Person Search by Separated Modeling and A Mask-Guided Two-Stream CNN Model
IEEE Transactions on Image Processing ( IF 10.8 ) Pub Date : 2020-02-19 , DOI: 10.1109/tip.2020.2973513 Di Chen , Shanshan Zhang , Wanli Ouyang , Jian Yang , Ying Tai
IEEE Transactions on Image Processing ( IF 10.8 ) Pub Date : 2020-02-19 , DOI: 10.1109/tip.2020.2973513 Di Chen , Shanshan Zhang , Wanli Ouyang , Jian Yang , Ying Tai
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. We also propose a Confidence Weighted Stream Attention method which further re-adjusts the relative importance of the two streams by incorporating the detection confidence. Furthermore, we simplify the whole pipeline by incorporating semantic segmentation into the re-ID network, which is trained by bounding boxes as weakly-annotated masks and identification labels simultaneously. From the experiments on two standard person search benchmarks i.e. CUHK-SYSU and PRW, we achieve mAP of 83.3% and 32.8% respectively, surpassing the state of the art by a large margin. The extensive ablation study and model inspection further justifies our motivation.
更新日期：2020-02-19
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