| Title | Abnormal Situation Detection using Global Surveillance Map |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Shin, Ho-Chul, Na, Kiin |
| Conference Name | 2021 International Conference on Information and Communication Technology Convergence (ICTC) |
| Date Published | oct |
| Keywords | abnormal detection, Deep Learning, Filtering, Human Behavior, information and communication technology, Metrics, Mobile Robot, probability, pubcrawl, resilience, Resiliency, security, Shape, video surveillance |
| 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%. |
| DOI | 10.1109/ICTC52510.2021.9621133 |
| Citation Key | shin_abnormal_2021 |
Abnormal Situation Detection using Global Surveillance Map