TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Collective Interaction Filtering Approach for Detection of Group in Diverse Crowded Scenes |
| 영문제목(English Title) |
Collective Interaction Filtering Approach for Detection of Group in Diverse Crowded Scenes |
| 저자(Author) |
Pei Voon Wong
Norwati Mustapha
Lilly Suriani Affendey
Fatimah Khalid
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| 원문수록처(Citation) |
VOL 13 NO. 02 PP. 0912 ~ 0928 (2019. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.
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| 키워드(Keyword) |
group detection
clustering
crowded scenes
trajectory
behavior analysis
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| 파일첨부 |
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