Adaptive detection and localization of moving objects in image sequences

In this paper we address two important problems in motion analysis: the detection of moving objects and their localization. A statistical approach is adopted in order to formulate these problems. For the first, the inter-frame difference is modelized by a mixture of two zero-mean generalized Gaussian 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. This method is also modelized using a statistical framework. The solution of the second problem is based on the observation of only two successive frames. Using the results of change detection an adaptive statistical model for the couple of image intensities is identified. For each problem two different multiscale algorithms are evaluated, and the labeling problem is solved using either iterated conditional modes (ICM) or highest confidence first (HCF) algorithms. For illustrating the efficiency of the proposed approach, experimental results are presented using synthetic and real video sequences.