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서지반출
Unsupervised Motion Pattern Mining for Crowded Scenes Analysis
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취소
  • Unsupervised Motion Pattern Mining for Crowded Scenes Analysis
  • Unsupervised Motion Pattern Mining for Crowded Scenes Analysis
저자명
Wang. Chongjing,Zhao. Xu,Zou. Yi,Liu. Yuncai
간행물명
KSII Transactions on internet and information systems : TIIS
권/호정보
2012년|6권 12호|pp.3315-3337 (23 pages)
발행정보
한국인터넷정보학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

Crowded scenes analysis is a challenging topic in computer vision field. How to detect diverse motion patterns in crowded scenarios from videos is the critical yet hard part of this problem. In this paper, we propose a novel approach to mining motion patterns by utilizing motion information during both long-term period and short interval simultaneously. To capture long-term motions effectively, we introduce Motion History Image (MHI) representation to access to the global perspective about the crowd motion. The combination of MHI and optical flow, which is used to get instant motion information, gives rise to discriminative spatial-temporal motion features. Benefitting from the robustness and efficiency of the novel motion representation, the following motion pattern mining is implemented in a completely unsupervised way. The motion vectors are clustered hierarchically through automatic hierarchical clustering algorithm building on the basis of graphic model. This method overcomes the instability of optical flow in dealing with time continuity in crowded scenes. The results of clustering reveal the situations of motion pattern distribution in current crowded videos. To validate the performance of the proposed approach, we conduct experimental evaluations on some challenging videos including vehicles and pedestrians. The reliable detection results demonstrate the effectiveness of our approach.