| Title | Abnormal Detection based on User Feedback for Abstracted Pedestrian Video |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Shin, Ho-Chul |
| Conference Name | 2019 International Conference on Information and Communication Technology Convergence (ICTC) |
| Date Published | oct |
| Keywords | abnormal detection, human factors, human in the loop, Image reconstruction, Pedestrian Behavior, probability, pubcrawl, robots, Scalability, security, Traffic Control, video surveillance |
| Abstract | In this study, we present the abstracted pedestrian behavior representation and abnormal detection method based on user feedback for pedestrian video surveillance system. Video surveillance data is large in size and difficult to process in real time. To solve this problem, we suggested a method of expressing the pedestrian behavior with abbreviated map. In the video surveillance system, false detection of an abnormal situation becomes a big problem. If surveillance user can guide the false detection case as human in the loop, the surveillance system can learn the case and reduce the false detection error in the future. We suggested user feedback based abnormal pedestrian detection method. By the suggested user feedback algorithm, the false detection can be reduced to less than 0.5%. |
| DOI | 10.1109/ICTC46691.2019.8939600 |
| Citation Key | shin_abnormal_2019 |
Abnormal Detection based on User Feedback for Abstracted Pedestrian Video