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Conference Paper
Abnormal Situation Detection using Global Surveillance Map
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- Authors
- Ho-chul Shin, Kiin Na
- Issue Date
- 2021-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.1-4
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- in this paper, we describe a method for detecting abnormal pedestrians or cars by expressing the behavioral characteristics of pedestrians on a global surveillance map in a video security system using CCTV and patrol robots. This method converts a large amount of video surveillance data into a compressed map shape format to efficiently transmit and process data. By using deep learning auto-encoder and CNN algorithm, pedestrians belonging to the abnormal category can be detected in two steps. In the case of the first-stage abnormal candidate extraction, the normal detection rate was 87.7%, the abnormal detection rate was 88.3%, and in the second stage abnormal candidate filtering, the normal detection rate was 99.8% and the abnormal detection rate was 96.5%.
- KSP Keywords
- Abnormal situation, Auto-Encoder(AE), Behavioral characteristics, CNN algorithm, First stage, Process data, Situation Detection, abnormal detection, deep learning(DL), detection rate(DR), security system