Abstract We propose an efficient way to account for spatial smoothness in foreground-background segmentation of video sequences. Most statistical background modeling techniques regard the pixels in an image as independent and disregard the fundamental concept of smoothness. In contrast, we model smoothness of the foreground and background with a Markov random field, in such a way that it can be globally optimized at video frame rate. As a background model, the mixture-of-Gaussian (MOG) model is adopted and enhanced with several improvements developed for other background models. Experimental results show that the MOG model is still competitive, and that segmentation with the smoothness prior outperforms other methods.
Smooth Foreground-Background Segmentation for Video Processing
Computer Vision – ACCV 2006 ; 7 ; 581-590
Lecture Notes in Computer Science ; 3852 , 7
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Background Model , Markov Random Fields , Video Processing , Foreground Object , Video Frame Rate Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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