DocumentCode :
639064
Title :
Dynamic background subtraction based on appearance and motion pattern
Author :
Haiyan Yin ; Hua Yang ; Hang Su ; Chongyang Zhang
Author_Institution :
Inst. of Image Commun. & Inf. Process., Shanghai Jiao Tong Univ., Shanghai, China
fYear :
2013
fDate :
15-19 July 2013
Firstpage :
1
Lastpage :
6
Abstract :
Moving objects detection plays a critical role in computer vision application, since it usually is the first phase in video processing. Most traditional methods are used in static scenes, however not perform well in dynamic situations, or they can only overcome limited perturbation. In this paper, we propose a stereo local binary pattern based on appearance and motion (SLBP-AM) descriptor for back-ground modeling and objects detection. We regard the motion of pixels as dynamic texture in ellipsoidal domain, and combine texture histograms in the XY, XT, YT planes in the ellipsoid as the new descriptor for background subtraction. Compared with traditional local binary pattern (LBP) descriptor, experiment results show that the new proposed method can not only be robust to slight disturbance, but also adapt quickly to the large-scale and sudden changes.
Keywords :
computer vision; image motion analysis; image texture; object detection; stereo image processing; video signal processing; SLBP-AM descriptor; appearance descriptor; back-ground modeling; computer vision application; dynamic background subtraction; dynamic texture; ellipsoidal domain; motion descriptor; motion pattern; moving objects detection; static scenes; stereo local binary pattern; texture histograms; video processing; Adaptation models; Dynamics; Ellipsoids; Histograms; Mathematical model; Robustness; Switches; Background modeling; dynamic texture; local binary pattern; objects detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on
Conference_Location :
San Jose, CA
Type :
conf
DOI :
10.1109/ICMEW.2013.6618403
Filename :
6618403
Link To Document :
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