Channel Attention Module and Weighted Local Feature Person Re-ID Network
Pages 124 - 128
Abstract
Person re-identification, as a new technology emerging in the field of intelligent detection and analysis in recent years, has received more and more attention from researchers. Cross-camera is one of the important features in the field of person re-identification, which makes the work very challenging. At present, most pedestrian recognition methods only use global features or local features. However, the performance boost that comes from combining the two features is ignored. Therefore, we propose a network that integrates global and local features. The added channel attention module makes the network pay more attention to the similar semantic information between channels and increase the discrimination ability. In addition, the local features containing discriminative information occupy a larger weight. Finally, extensive experiments have verified the effectiveness of our method, and our method has shown state-of-the-art results in three main datasets: Market-1501, DukeMTMC-reID, and CUHK03.
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Published In
January 2021
178 pages
ISBN:9781450387613
DOI:10.1145/3453800
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 June 2021
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- Research-article
- Research
- Refereed limited
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- Natural Science Foundation of China
- Shandong Graduate Education Innovation Project
Conference
ICMLSC '21
ICMLSC '21: 2021 The 5th International Conference on Machine Learning and Soft Computing
January 29 - 31, 2021
Da Nang, Viet Nam
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