We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK


93% of Lancaster students go into work or further study within six months of graduating

Home > Research > Publications & Outputs > A Real-time Approach for Novelty Detection and ...
View graph of relations

« Back

A Real-time Approach for Novelty Detection and Trajectories Analysis for Anomaly Recognition in Video Surveillance Systems

Research output: Contribution in Book/Report/ProceedingsPaper


Publication date2012
Host publicationEvolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on
Number of pages6
ISBN (Electronic)978-1-4673-1726-9
ISBN (Print)978-1-4673-1728-3
Original languageEnglish


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.