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Learning motion patterns in crowded scenes using motion flow field

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3 Author(s)
Min Hu ; Comput. Vision Lab., Univ. of Central Florida, FL, USA ; Ali, S. ; Shah, M.

Learning typical motion patterns or activities from videos of crowded scenes is an important visual surveillance problem. To detect typical motion patterns in crowded scenarios, we propose a new method which utilizes the instantaneous motions of a video, i.e, the motion flow field, instead of long-term motion tracks. The motion flow field is a union of independent flow vectors computed in different frames. Detecting motion patterns in this flow field can therefore be formulated as a clustering problem of the motion flow fields, where each motion pattern consists of a group of flow vectors participating in the same process or motion. We first construct a directed neighborhood graph to measure the closeness of flow vectors. A hierarchical agglomerative clustering algorithm is applied to group flow vectors into desired motion patterns.

Published in:

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Date of Conference:

8-11 Dec. 2008