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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.2973513
|2 doi
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|a eng
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| 100 |
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|a Chen, Di
|e verfasserin
|4 aut
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| 245 |
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|a Person Search by Separated Modeling and A Mask-Guided Two-Stream CNN Model
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|c 2020
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a 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
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|a Journal Article
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|a Zhang, Shanshan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ouyang, Wanli
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jian
|e verfasserin
|4 aut
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| 700 |
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|a Tai, Ying
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2020) vom: 19. Feb.
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|x 1941-0042
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|g year:2020
|g day:19
|g month:02
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|u http://dx.doi.org/10.1109/TIP.2020.2973513
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