A model-based 3-D tracking of rigid objects from a sequence of multiple perspective views

Abstract

A method of tracking multiple objects of known geometry using multiple cameras is proposed. Our approach differs from the previous approaches in that the object geometry is tightly integrated into the tracking process. The major contribution is threefold: Firstly, multiple cameras are used to improve the accuracy of the estimated posture parameters. Additional formalism required by considering multiple images is nicely integrated into the tracking model, and is handled effectively. Secondly, the feature tracking is facilitated by integrating the measurement and dynamic models into the matching process, thereby improving the accuracy and robustness of the feature correspondence. Thirdly, ambiguities that may arise in the course of the feature matching are resolved by the statistical analysis and the visibility test. The entire process from the image sequence to the posture parameters has been completely automated into a single, seamless process, and has been extensively tested on synthetic and real images.