Online Detection of Abnormal Events in Video Streams. (24th December 2013)

• Record Type: Journal Article
• Authors: Wang, Tian; Chen, Jie; Snoussi, Hichem
• Other Names: Zhou Yi Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2013(2013)
• Publication Date: 2013-12-24
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2013/837275
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : We propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM) firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique.