Recursive resolving algorithm for multiple stereo and motion matches

Abstract

In this paper, a recursive algorithm is proposed to resolve the multiple stereo and motion matches with stereo motion sequence. An extended Kalman filter is applied to filter out some of the noise and to estimate recursively both three-dimensional (3D) relative motion and the depth of objects moving in 3D space. It is assumed that the objects move independently of each other, and have a small enough size to represent them as point tokens. We derive the conditions under which ambiguity in 3D reconstruction occurs, and discuss how to escape the pitfalls with the help of motion information. Virtual objects are generated from the ambiguous multiple stereo matches, and tracked through stereo motion sequences. When any stereo match is not observed within the search range predicted by the virtually generated token, the token becomes resolved to be a false match. The innovation in the Kalman filter is used to select the best one from among the tokens sharing an observation in a sequential manner. Simulation is carried out to show the capability of resolving multiple stereo and motion matches for a synthetic stereo sequence. Experimental results with a real stereo image sequence are presented to show the effectiveness of the algorithm.