Nonparametric motion model with applications to camera motion pattern classification

Motion information is a powerful cue for visual perception. In the context of video indexing and retrieval, motion content serves as a useful source for compact video representation. There has been a lot of literature about parametric motion models. However, it is hard to secure a proper parametric assumption in a wide range of video scenarios. Diverse camera shots and frequent occurrences of bad optical flow estimation motivate us to develop nonparametric motion models. In this paper, we employ the mean shift procedure to propose a novel nonparametric motion representation. With this compact representation, various motion characterization tasks can be achieved by machine learning. Such a learning mechanism can not only capture the domain-independent parametric constraints, but also acquire the domain-dependent knowledge to tolerate the influence of bad dense optical flow vectors or block-based MPEG motion vector fields (MVF). The proposed nonparametric motion model has been applied to camera motion pattern classification on 23191 MVF extracted from MPEG-7 dataset.