Recognizing events in an automated surveillance system

Date

2006-01-01

Author

Orten, Birant
Alatan, Abdullah Aydın
Çiloğlu, Tolga

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Event recognition is probably the ultimate purpose of an automated surveillance system. In this paper, hidden Markov models (HMM) are utilized to recognize the nature of an event occurring in a scene. For this purpose, object trajectories, which are obtained through a successful track, are obtained as a sequence of flow vectors that contain instantaneous velocity and location information. These vectors are clustered by K-means algorithm to obtain a prototype representation. HMMs are trained with sequences obtained from usual motion patterns and abnormality is detected by measuring distances to these models. In order to specify the number of models automatically, a novel approach is proposed which utilizes the clues provided by centroid clustering. Preliminary experimental results are promising for detecting abnormal events.

URI

https://hdl.handle.net/11511/52760

Journal

MULTIMEDIA CONTENT REPRESENTATION, CLASSIFICATION AND SECURITY

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

B. Orten, A. A. Alatan, and T. Çiloğlu, “Recognizing events in an automated surveillance system,” MULTIMEDIA CONTENT REPRESENTATION, CLASSIFICATION AND SECURITY, pp. 434–441, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52760.