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231225s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2020.3032542
|2 doi
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|a pubmed24n1054.xml
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|a (DE-627)NLM316489301
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|a (NLM)33079658
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Yan, Yichao
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Learning Multi-Attention Context Graph for Group-Based Re-Identification
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| 264 |
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1 |
|c 2023
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 07.05.2023
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|a Date Revised 07.05.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. However, most existing methods focus on (single) person re-identification (re-id), ignoring the fact that people often walk in groups in real scenarios. In this work, we take a step further and consider employing context information for identifying groups of people, i.e., group re-id. On the one hand, group re-id is more challenging than single person re-id, since it requires both a robust modeling of local individual person appearance (with different illumination conditions, pose/viewpoint variations, and occlusions), as well as full awareness of global group structures (with group layout and group member variations). On the other hand, we believe that person re-id can be greatly enhanced by incorporating additional visual context from neighboring group members, a task which we formulate as group-aware (single) person re-id. In this paper, we propose a novel unified framework based on graph neural networks to simultaneously address the above two group-based re-id tasks, i.e., group re-id and group-aware person re-id. Specifically, we construct a context graph with group members as its nodes to exploit dependencies among different people. A multi-level attention mechanism is developed to formulate both intra-group and inter-group context, with an additional self-attention module for robust graph-level representations by attentively aggregating node-level features. The proposed model can be directly generalized to tackle group-aware person re-id using node-level representations. Meanwhile, to facilitate the deployment of deep learning models on these tasks, we build a new group re-id dataset which contains more than 3.8K images with 1.5K annotated groups, an order of magnitude larger than existing group re-id datasets. Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks
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|a Journal Article
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| 700 |
1 |
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|a Qin, Jie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ni, Bingbing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Jiaxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Li
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Fan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zheng, Wei-Shi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Xiaokang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shao, Ling
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 6 vom: 12. Juni, Seite 7001-7018
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:6
|g day:12
|g month:06
|g pages:7001-7018
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2020.3032542
|3 Volltext
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