TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Unsupervised Motion Pattern Mining for Crowded Scenes Analysis |
| 영문제목(English Title) |
Unsupervised Motion Pattern Mining for Crowded Scenes Analysis |
| 저자(Author) |
Chongjing Wang
Xu Zhao
Yi Zou
Yuncai Liu
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| 원문수록처(Citation) |
VOL 06 NO. 12 PP. 3315 ~ 3337 (2012. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
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.
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| 키워드(Keyword) |
crowd analysis
motion pattern
motion history image
hierarchical clustering
optical flow
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| 파일첨부 |
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