Motion segmentation using interest points

In this paper, we present a novel approach to motion segmentation by using interest points. Distinctive image features, so called interest points, were extracted in each image of the sequences and tracked using a Kalman filter. The interest points are then organized in a undirected graph. This connected structure can be used to describe the spatial relationship of interest points. An edge scoring algorithm is introduced that favors edges connecting interest points lying on the same object and punishing bridging edges, e.q. edges connecting different objects. To do so, we will introduce our so called ldquohomogeneous scale assumptionrdquo that is used to calculate scores for each edge in the graph. The resulting connected sub-structures in the graph are mathematically described by a radial map and then again tracked using a Kalman filter to further increase robustness. The presented algorithm is capable of working at 30 Hz and is thus feasible in a wide variety of applications.