DocumentCode :
603073
Title :
Histograms of optical flow orientation for abnormal events detection
Author :
Tian Wang ; Snoussi, Hichem
Author_Institution :
LM2S, Univ. de Technol. de Troyes, Troyes, France
fYear :
2013
fDate :
15-17 Jan. 2013
Firstpage :
45
Lastpage :
52
Abstract :
In this paper, we propose an algorithm to detect abnormal events based on video streams. The algorithm is based on histograms of the orientation of optical flow descriptor and one-class SVM classifier. We introduce grids of Histograms of the Orientation of Optical Flow (HOF) as the descriptors for motion information of the monolithic video frame. The one-class SVM, after a learning period characterizing normal behaviors, detects the abnormality which is considered as the event needed to be recognized in the current frame. Extensive testing on dataset corroborates the effectiveness of the proposed detection method.
Keywords :
object detection; support vector machines; video streaming; HOF orientation; abnormal event detection; histograms of the orientation of optical flow orientation; learning period; monolithic video frame; motion information; normal behaviors; one-class SVM classifier; optical flow descriptor; video streams; Feature extraction; Histograms; Legged locomotion; Markov processes; Optical imaging; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Performance Evaluation of Tracking and Surveillance (PETS), 2013 IEEE International Workshop on
Conference_Location :
Clearwater, FL
ISSN :
2157-491X
Print_ISBN :
978-1-4673-5649-7
Electronic_ISBN :
2157-491X
Type :
conf
DOI :
10.1109/PETS.2013.6523794
Filename :
6523794
Link To Document :
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