Online least squares one-class support vector machines-based abnormal visual event detection

Tian Wang, Jie Chen, Yi Zhou, Hichem Snoussi

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

The abnormal event detection problem is an important subject in real-time video surveillance. In this paper, we propose a novel online one-class classification algorithm, online least squares one-class support vector machine (online LS-OC-SVM), combined with its sparsified version (sparse online LS-OC-SVM). LS-OC-SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. The online LS-OC-SVM learns a training set with a limited number of samples to provide a basic normal model, then updates the model through remaining data. In the sparse online scheme, the model complexity is controlled by the coherence criterion. The online LS-OC-SVM is adopted to handle the abnormal event detection problem. Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information, then is classified into a normal or an abnormal frame. Experiments are conducted, on a two-dimensional synthetic distribution dataset and a benchmark video surveillance dataset, to demonstrate the promising results of the proposed online LS-OC-SVM method.

Original languageEnglish
Pages (from-to)17130-17155
Number of pages26
JournalSensors
Volume13
Issue number12
DOIs
StatePublished - 12 Dec 2013
Externally publishedYes

Keywords

  • Abnormal detection
  • Covariance matrix descriptor
  • Online least squares one-class SVM
  • Optical flow

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