Multiple Segmentation of Moving Objects by Quasi-simultaneous Parametric Motion Estimation

This paper presents a new framework for the motion segmentation and estimation task on sequences of two grey images without a priori information of the number of moving regions present in the sequence. The proposed algorithm combines temporal information, by using an accurate Generalized Least-Squares Motion Estimation process and spatial information by using an inlier/outlier classification process which classifies regions of pixels, in a first step, and the pixels directly, in a second step, into the different motion models present in the sequence. The performance of the algorithm has been tested on synthetic and real images with multiple objects undergoing different types of motion.