TY - GEN
T1 - Belief propagation in a 3D spatio-temporal MRF for moving object detection
AU - Yin, Zhaozheng
AU - Collins, Robert
PY - 2007/10/12
Y1 - 2007/10/12
N2 - Previous pixel-level change detection methods either contain a background updating step that is costly for moving cameras (background subtraction) or can not locate object position and shape accurately (frame differencing). In this paper we present a Belief Propagation approach for moving object detection using a 3D Markov Random Field (MRF) model. Each hidden state in the 3D MRF model represents a pixel's motion likelihood and is estimated using message passing in a 6-connected spatio-temporal neighborhood. This approach deals effectively with difficult moving object detection problems like objects camouflaged by similar appearance to the background, or objects with uniform color that frame difference methods can only partially detect. Three examples are presented where moving objects are detected and tracked successfully while handling appearance change, shape change, varied moving speed/direction, scale change and occlusion/clutter.
AB - Previous pixel-level change detection methods either contain a background updating step that is costly for moving cameras (background subtraction) or can not locate object position and shape accurately (frame differencing). In this paper we present a Belief Propagation approach for moving object detection using a 3D Markov Random Field (MRF) model. Each hidden state in the 3D MRF model represents a pixel's motion likelihood and is estimated using message passing in a 6-connected spatio-temporal neighborhood. This approach deals effectively with difficult moving object detection problems like objects camouflaged by similar appearance to the background, or objects with uniform color that frame difference methods can only partially detect. Three examples are presented where moving objects are detected and tracked successfully while handling appearance change, shape change, varied moving speed/direction, scale change and occlusion/clutter.
UR - http://www.scopus.com/inward/record.url?scp=35148872716&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35148872716&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2007.383184
DO - 10.1109/CVPR.2007.383184
M3 - Conference contribution
AN - SCOPUS:35148872716
SN - 1424411807
SN - 9781424411801
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
BT - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
T2 - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Y2 - 17 June 2007 through 22 June 2007
ER -