Particle Filtering Based Estimation of Consistent Motion and Disparity with Reduced Search Points

Abstract

A particle filtering based block-wise estimation method for estimation of motion in a video sequence and joint estimation of disparity and motion in a stereo video sequence is proposed. Parameters of motion and disparity of a block in a sequence are defined as a state, and evolution of the state with respect to the block index is tracked with particle filtering. The state is assumed to be dependent on the states of neighboring blocks. Estimated motion and disparity fields are consistent and suitable for intermediate frame or view generation. The particle filter provides a method to effectively sample the search space. The particles are concentrated in regions where the probability density function for the state has large values. Hence, the locations of the particles are good candidates for search points of a fast search method. The proposed method can estimate motion and disparity with a fraction of search points necessary for conventional estimation methods.