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A real-time approach for novelty detection and trajectories analysis for anomaly recognition in video surveillance systems

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2 Author(s)
Pouria Sadeghi-Tehran ; School of Computing and Communications, Infolab21, Lancaster University, United Kingdom, LA1 4WA ; Plamen Angelov

In this paper, we present a novel approach for automatic object detection and also using on-line trajectory clustering for RT anomaly detection in video streams. The proposed approach is based on two main steps. In the first step, a recently introduced approach called Recursive Density Estimation (RDE) is used for novelty detection. This method is using a Cauchy type of kernel which works on a frame-by-frame basis and does not require a pre-defined threshold to identify objects. In the second step, multifeature object trajectory is clustered on-line to identify anomalies in video streams. To identify an anomaly, first the trajectories are transformed into a set of features in a space to which eClustering approach identifies the modes and the corresponding clusters. At the end, by using cluster fusion the final common pattern is estimated and any sparse trajectories are considered as anomalous.

Published in:

Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on

Date of Conference:

17-18 May 2012