Abstract In this paper we give a new model for foreground-back-ground-shadow separation. Our method extracts the faithful silhouettes of foreground objects even if they have partly background like colors and shadows are observable on the image. It does not need any a priori information about the shapes of the objects, it assumes only they are not point-wise. The method exploits temporal statistics to characterize the background and shadow, and spatial statistics for the foreground. A Markov Random Field model is used to enhance the accuracy of the separation. We validated our method on outdoor and indoor video sequences captured by the surveillance system of the university campus, and we also tested it on well-known benchmark videos.
Markovian Framework for Foreground-Background-Shadow Separation of Real World Video Scenes
Computer Vision – ACCV 2006 ; 10 ; 898-907
Lecture Notes in Computer Science ; 3851 , 10
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Markov Random Fields , Foreground Object , Foreground Pixel , Markov Random Fields Model , Foreground Detection Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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