Title

Learning Motion Patterns In Crowded Scenes Using Motion Flow Field

Abstract

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. © 2008 IEEE.

Publication Date

1-1-2008

Publication Title

Proceedings - International Conference on Pattern Recognition

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/icpr.2008.4761183

Socpus ID

77957955072 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77957955072

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