Abstract This paper presents a new approach to automatic segmentation of foreground objects with shadow removal from an image sequence by integrating techniques of background subtraction and motion-based foreground segmentation. First, a region-based motion segmentation algorithm is proposed to obtain a set of motion-coherence regions and the correspondence among regions at different time instants. Next, we formulate the foreground detection problem as a graph labeling over a region adjacency graph (RAG) based on Markov random fields (MRFs) statistical framework. A background model representing the background scene is built and then is used to model a likelihood energy. Besides the background model, the temporal and spatial coherence are also maintained by modeling it as a prior energy. Finally, a labeling is obtained by maximizing a posterior energy of the MRFs. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.
Region-Level Motion-Based Foreground Detection with Shadow Removal Using MRFs
Computer Vision – ACCV 2006 ; 10 ; 878-887
Lecture Notes in Computer Science ; 3851 , 10
2006-01-01
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Background Model , Spatial Coherence , Foreground Object , Segmented Region , Motion Segmentation 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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