Non parametric motion recognition using temporal multiscale Gibbs models

We present an original approach for non parametric motion analysis in image sequences. It relies on the statistical modeling of distributions of local motion-related measurements computed over image sequences. Contrary to previously proposed methods, the use of temporal multiscale Gibbs models allows us to handle in a unified statistical framework both spatial and temporal aspects of motion content. The important feature of our probabilistic scheme is to make the exact computation of conditional likelihood functions feasible and simple. It enables us to straightforwardly achieve model estimation according to the ML criterion and to benefit from a statistical point of view for classification issues. We have conducted motion recognition experiments over a large set of real image sequences comprising various motion types such as temporal texture samples, human motion examples and rigid motion situations.