A spatio-temporal filtering approach to motion segmentation

In this paper, a new frequency-based approach to motion segmentation is presented. The proposed technique represents the sequence as a spatio-temporal volume, where a moving object corresponds to a three-dimensional object. In order to detect the "3D volumes" corresponding to significant motions, a new scheme based on a band-pass filtering with a set of logGabor spatio-temporal filters is used. It is well known that one of the main problems of these approaches is that a filter response varies with the spatial orientation of the underlying signal. To solve this spatial dependency, the proposed model allows to recombine information of motions that has been separated in several filter responses due to its spatial structure. For this purpose, motions are detected as invariance in statistical structure across a range of spatio-temporal frequency bands. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies.