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Detection of Moving Objects in Crowded Scenes using Trajectory Clustering via Conditional Random Fields Framework  

Kim, Hyeong-Ki ((주) 만도 ADAS 사업실 SYSTEM3팀)
Lee, Gwang-Gook (한양대학교 전자컴퓨터통신공학과)
Kim, Whoi-Yul (한양대학교 전자통신컴퓨터공학부)
Publication Information
Journal of Korea Multimedia Society / v.13, no.8, 2010 , pp. 1128-1141 More about this Journal
Abstract
This paper proposes a method of moving object detection in crowded scene using clustered trajectory. Unlike previous appearance based approaches, the proposed method employes motion information only to isolate moving objects. In the proposed method, feature points are extracted from input frames first and then feature tracking is followed to create feature trajectories. Based on an assumption that feature points originated from the same objects shows similar motion as the object moves, the proposed method detects moving objects by clustering trajectories of similar motions. For this purpose an energy function based on spatial proximity, motion coherence, and temporal continuity is defined to measure the similarity between two trajectories and the clustering is achieved by minimizing the energy function in CRFs (conditional random fields). Compared to previous methods, which are unable to separate falsely merged trajectories during the clustering process, the proposed method is able to rearrange the falsely merged trajectories during iteration because the clustering is solved my energy minimization in CRFs. Experiment results with three different crowded scenes show about 94% detection rate with 7% false alarm rate.
Keywords
Moving object detection; trajectory clustering; Conditional random fields;
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