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Belief Propagation in a 3D Spatio-temporal MRF for Moving Object Detection

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Belief Propagation in a 3D Spatio-temporal MRF for Moving Object Detection
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.
Zhaozheng Yin, Robert T. Collins
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Zhaozheng Yin, Robert T. Collins
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