Moving Object Segmentation Using Dynamic 3D Graph Cuts and GMM

It is one of the most challenging problems in computer vision how to segment moving objects accurately. In this paper, we present a novel approach to segment moving objects with edge information and temporal information using 3D graph cuts model when cameras is fixed. Moving object segmentation is modeled as finding a minimum energy of 3D graph. Our algorithm assigns n-links in 3D graph according to spatial gradient in same frame and temporal gradient in neighboring frames. Gaussian mixture model is used to assign t-links with edge difference term and shadow elimination term. Finally, a dynamic graph cuts algorithm is used to find the minimum cut of 3D graph and segments moving objects in image sequences. Experiments show that our approach achieves nice performance