Urban waterlogging identification system based on public surveillance video

Peng Hu; Weizhong Sun; Yingqi Cai; Guangsheng Wu

doi:10.1117/12.2577477

10 November 2020 Urban waterlogging identification system based on public surveillance video

Peng Hu, Weizhong Sun, Yingqi Cai, Guangsheng Wu

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158410 (2020) https://doi.org/10.1117/12.2577477
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Aiming at the problems of limited coverage, limited construction and high maintenance cost of the traditional urban waterlogging early warning system, a large-scale waterlogging automatic recognition system based on urban public security monitoring video is designed. By manually organizing the existing urban policing video image data, a set of flood image data set is constructed to train and evaluate a variety of deep convolutional neural networks, and the networks with moderate recognition performance and computational cost are deployed in the actual identification system. The experimental results show that the system can identify the image with 97.72% accuracy, and has a recall rate of 95.16%, and a single image recognition server can support 135 video points, and support elastic scaling, which can meet the needs of large-scale deployment.

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Peng Hu, Weizhong Sun, Yingqi Cai, and Guangsheng Wu "Urban waterlogging identification system based on public surveillance video", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158410 (10 November 2020); https://doi.org/10.1117/12.2577477

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KEYWORDS

Video

Video surveillance

Performance modeling

Convolution

Data modeling

Neural networks

Surveillance

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