TY - JOUR
T1 - Region-level motion-based background modeling and subtraction using MRFs
AU - Huang, Shih Shinh
AU - Fu, Li Chen
AU - Hsiao, Pei Yung
PY - 2007/5
Y1 - 2007/5
N2 - This paper presents a new approach to automatic segmentation of foreground objects 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 classification problem as a graph labeling over a region adjacency graph 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, a temporal coherence is also maintained by modeling it as the prior energy. On the other hand, color distributions of two neighboring regions are taken into consideration to impose spatial coherence. Then, the a priori energy of MRFs takes both spatial and temporal coherence into account to maintain the continuity of our segmentation. Finally, a labeling is obtained by maximizing the a posteriori energy of the MRFs. Under such formulation, we integrate two different kinds of techniques in an elegant way to make the foreground detection more accurate. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.
AB - This paper presents a new approach to automatic segmentation of foreground objects 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 classification problem as a graph labeling over a region adjacency graph 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, a temporal coherence is also maintained by modeling it as the prior energy. On the other hand, color distributions of two neighboring regions are taken into consideration to impose spatial coherence. Then, the a priori energy of MRFs takes both spatial and temporal coherence into account to maintain the continuity of our segmentation. Finally, a labeling is obtained by maximizing the a posteriori energy of the MRFs. Under such formulation, we integrate two different kinds of techniques in an elegant way to make the foreground detection more accurate. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.
KW - Background subtraction
KW - Markov random fields (MRFs)
KW - Motion-based segmentation
UR - http://www.scopus.com/inward/record.url?scp=34247333309&partnerID=8YFLogxK
U2 - 10.1109/TIP.2007.894246
DO - 10.1109/TIP.2007.894246
M3 - 文章
C2 - 17491472
AN - SCOPUS:34247333309
SN - 1057-7149
VL - 16
SP - 1446
EP - 1456
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 5
ER -