Detection of Crowd Escape Behavior in Surveillance Video 


Vol. 39,  No. 8, pp. 731-737, Aug.  2014


PDF
  Abstract

This paper presents abnormal behavior detection in crowd within surveillance video. We have defined below two cases as a abnormal behavior; first as a sporadically spread phenomenon and second as a sudden running in same direction. In order to detect these two abnormal behaviors, we first extract the motion vector and propose a new descriptor which is combined MHOF(Multi-scale Histogram of Optical Flow) and DCHOF(Directional Change Histogram of Optical Flow). Also, binary classifier SVM(Support Vector Machine) is used for detection. The accuracy of the proposed algorithm is evaluated by both UMN and PETS 2009 dataset and comparisons with the state-of-the-art method validate the advantages of our algorithm.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

J. Park and S. Kwak, "Detection of Crowd Escape Behavior in Surveillance Video," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 8, pp. 731-737, 2014. DOI: .

[ACM Style]

Junwook Park and Sooyeong Kwak. 2014. Detection of Crowd Escape Behavior in Surveillance Video. The Journal of Korean Institute of Communications and Information Sciences, 39, 8, (2014), 731-737. DOI: .

[KICS Style]

Junwook Park and Sooyeong Kwak, "Detection of Crowd Escape Behavior in Surveillance Video," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 8, pp. 731-737, 8. 2014.

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880