Fast Marching to Moving Object Location

In this paper we address two important problems in motion analysis: the detection of moving objects and their localization. Statistical and level set approaches are adopted in order to formulate these problems. For the change detection problem, the inter-frame di erence is modeled by a mixture of two zero-mean Laplacian distributions. At rst, statistical tests using criteria with negligible error probability are used for labeling as many as possible sites as changed or unchanged. All the connected components of the labeled sites are seed regions, which give the initial level sets, for which velocity elds for label propagation are provided. We introduce a new multi-label fast marching algorithm for expanding competitive regions. The solution of the localization problem is based on the map of changed pixels previously extracted. The boundary of the moving object is determined by a level set algorithm, which is initialized by two curves evolving in converging opposite directions. The sites of curve contact determine the position of the object boundary. For illustrating the e ciency of the proposed approach, experimental results are presented using real video sequences.