Motion recognition using spatio-temporal random walks in sequence of 2D motion-related measurements

This paper describes an original approach for non parametric motion analysis in image sequences. It relies on a statistical modeling of distributions of local motion-related measurements, computed over image sequences, resulting from spatio-temporal random walks. It handles in a single probabilistic framework both spatial and temporal properties of motion content. The important feature of our method is to make feasible the exact computation of conditional likelihood functions. We have carried out motion recognition experiments over a large set of real image sequences comprising various motion types.