期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 25, 期 10, 页码 1612-1623出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2015.2395752
关键词
Crowded scenes; motion influence map; unusual activity detection; vision-based surveillance
资金
- ICT Research and Development Program within the Ministry of Science, Information and Communications Technologies and Future Planning/Institute for Information and Communications Technology Promotion [14-824-09-005]
- Ministry of Trade, Industry, and Energy [10041629]
- National Agenda Project through the Korea Research Council of Fundamental Science and Technology
- Korea Evaluation Institute of Industrial Technology (KEIT) [10041629] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Ministry of Public Safety & Security (MPSS), Republic of Korea [B0101-15-0552] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Council of Science & Technology (NST), Republic of Korea [NAP-10-3-KRISS] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this paper, we propose a novel method for unusual human activity detection in crowded scenes. Specifically, rather than detecting or segmenting humans, we devised an efficient method, called a motion influence map, for representing human activities. The key feature of the proposed motion influence map is that it effectively reflects the motion characteristics of the movement speed, movement direction, and size of the objects or subjects and their interactions within a frame sequence. Using the proposed motion influence map, we further developed a general framework in which we can detect both global and local unusual activities. Furthermore, thanks to the representational power of the proposed motion influence map, we can localize unusual activities in a simple manner. In our experiments on three public datasets, we compared the performances of the proposed method with that of other state-of-the-art methods and showed that the proposed method outperforms these competing methods.
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