Next Article in Journal
Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring
Previous Article in Journal
Informational Analysis for Compressive Sampling in Radar Imaging
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(4), 7156-7171; doi:10.3390/s150407156

Detection of Abnormal Events via Optical Flow Feature Analysis

1
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2
Institut Charles Delaunay-LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, Troyes 10004, France
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 17 January 2015 / Accepted: 16 March 2015 / Published: 24 March 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [980 KB, uploaded 24 March 2015]   |  

Abstract

In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. View Full-Text
Keywords: abnormal detection; optical flow; one-class SVM; KPCA abnormal detection; optical flow; one-class SVM; KPCA
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, T.; Snoussi, H. Detection of Abnormal Events via Optical Flow Feature Analysis. Sensors 2015, 15, 7156-7171.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top