We present a probabilistic model for motion estimation in which motion characteristics are inferred on the basis of a finite mixture of motion models. The model is graphically represented in the form of a pairwise Markov Random Field network upon which a Loopy Belief Propagation algorithm is exploited to perform inference. Experiments on different video clips are presented and discussed.
Citation:
Giuseppe Boccignone, Angelo Marcelli, Paolo Napoletano, Mario Ferraro, "Motion Estimation via Belief Propagation," iciap, pp.55-60, 14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007