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학술지 Robust Multi-person Tracking for Real-Time Intelligent Video Surveillance
Cited 37 time in scopus Download 9 time Share share facebook twitter linkedin kakaostory
저자
최진우, 문대성, 유장희
발행일
201506
출처
ETRI Journal, v.37 no.3, pp.551-561
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.15.0114.0629
협약과제
13VS1100, 사람에 의한 안전위협의 실시간 인지를 위한 능동형 영상보안 서비스용 원거리 (CCTV 주간환경 5m이상) 사람 식별 및 검색 원천기술 개발, 유장희
초록
We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.
키워드
Background modeling, Multiple-object tracking, Particle filter, Pedestrian detection, Real-time applications, Video surveillance applications.
KSP 제안 키워드
Background Modeling, Data association, Multi-person tracking, Multiple object tracking, Object tracking algorithm, Real-time application, Region Of Interest(ROI), Surveillance applications, Two-Step, computation time, intelligent video surveillance