Adaptive detection of moving objects using multiscale techniques
In this paper we address an important issue in motion analysis: the detection of moving objects. A statistical approach is adopted in order to formulate the problem. The inter-frame difference is modeled by a mixture of Laplacian distributions, and a Gibbs random field is used for describing the label set. A new method to determine the regularization parameter is proposed, based on a voting technique. Then two different multiscale algorithms are evaluated, and the labeling problem is solved using either ICM (iterated conditional modes) or HCF (highest confidence first) algorithms. Experimental results are provided using synthetic and real video sequences.