Sadeghi-Tehran, Pouria and Angelov, Plamen (2012) A Real-time Approach for Novelty Detection and Trajectories Analysis for Anomaly Recognition in Video Surveillance Systems. In: Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on. IEEE, pp. 108-113. ISBN 978-1-4673-1728-3Full text not available from this repository.
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.
|Item Type:||Contribution in Book/Report/Proceedings|
|Uncontrolled Keywords:||trajectory analysis ; behaviour analysis ; video analytics|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||20 Jul 2012 14:51|
|Last Modified:||25 Feb 2017 02:17|
Actions (login required)