Cited By
View all- Irene SJohn Prakash ARhymend Uthariaraj V(2024)Person search over security video surveillance systems using deep learning methodsImage and Vision Computing10.1016/j.imavis.2024.104930143:COnline publication date: 2-Jul-2024
An Unsupervised Person Search Method for Video Surveillance
Pages 625 - 630
Abstract
We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. Thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. The spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features.
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Published In
March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 July 2022
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- Research-article
- Research
- Refereed limited
Funding Sources
- Open Project of Key Laboratory of System Control and Information Processing Ministry of Education
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
Conference
ICCAI '22
ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence
March 18 - 21, 2022
Tianjin, China
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View all- Irene SJohn Prakash ARhymend Uthariaraj V(2024)Person search over security video surveillance systems using deep learning methodsImage and Vision Computing10.1016/j.imavis.2024.104930143:COnline publication date: 2-Jul-2024
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