A Fast Active Contour Tracking Method Based on Gaussian Mixture Model
This paper proposed a Gaussian mixture model based gradient level set method (GMM-GLS) for moving target contour tracking in video sequences to handle automatic initialization and background variation. In contrast with conventional level set models, adaptive GMM background subtraction is applied to get the rough location of moving target as foreground in current frame. And more accurate mask image according to the rough location of foreground with dilatation operation indicates the initialization contour of level set evolution. Then, the gradient level set model can evolve the curve quickly and ensure more accurate convergence to the target contour in tracking procedure. Based on this accurate mask, the GMM-GLS method can greatly reduce the uncertain iteration time in curve convergence and optimize the initialization of GLS eliminating the interferential background. Experimental results on many real-world video sequences validate that our approach greatly improves the performance of object contour tracking.
KeywordsGeometric active contours Level set Gaussian mixture model
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