Nonparametric motion model

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 improper optical flow estimation or block matching motivate us to develop nonparametric motion models. In this demonstration, we present a novel nonparametric motion model. The unique features mainly include: 1) Instead of computationally expensive and vulnerable parametric regression our proposed model bases the motion characterization on the classification of motion patterns; 2) we employ machine learning to capture the knowledge of recognizing camera motion patterns from bad motion vector fields (MVF); and 3) with the mean shift filtering our proposed motion representation elegantly incorporates the spatial-range information for noise removal and discontinuity preserving smoothing of MVF. Promising results have been achieved on two tasks: 1) camera motion pattern recognition on 23191 MVFs and 2) recognition of the intensity of motion activity on 622 video segments culled from the MPEG-7 dataset.