DocumentCode :
2399264
Title :
Background subtraction in highly dynamic scenes
Author :
Mahadevan, Vijay ; Vasconcelos, Nuno
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of California, San Diego, La Jolla, CA
fYear :
2008
fDate :
23-28 June 2008
Firstpage :
1
Lastpage :
6
Abstract :
A new algorithm is proposed for background subtraction in highly dynamic scenes. Background subtraction is equated to the dual problem of saliency detection: background points are those considered not salient by suitable comparison of object and background appearance and dynamics. Drawing inspiration from biological vision, saliency is defined locally, using center-surround computations that measure local feature contrast. A discriminant formulation is adopted, where the saliency of a location is the discriminant power of a set of features with respect to the binary classification problem which opposes center to surround. To account for both motion and appearance, and achieve robustness to highly dynamic backgrounds, these features are spatiotemporal patches, which are modeled as dynamic textures. The resulting background subtraction algorithm is fully unsupervised, requires no training stage to learn background parameters, and depends only on the relative disparity of motion between the center and surround regions. This makes it insensitive to camera motion. The algorithm is tested on challenging video sequences, and shown to outperform various state-of-the-art techniques for background subtraction.
Keywords :
feature extraction; image motion analysis; image sequences; image texture; natural scenes; object detection; pattern classification; spatiotemporal phenomena; video signal processing; background subtraction; binary classification problem; biological vision; camera motion; center-surround computations; discriminant formulation; dynamic textures; feature contrast; highly dynamic scenes; object appearance; saliency detection; spatiotemporal patches; video sequences; Biology computing; Cameras; Computer vision; Engineering drawings; Layout; Object detection; Object recognition; Robot vision systems; Robustness; Spatiotemporal phenomena;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
ISSN :
1063-6919
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2008.4587576
Filename :
4587576
Link To Document :
بازگشت