Coupling multi-view dynamics with mixtures of Gaussians
Paolo Remagnino, Dorothy Monekosso, Gian Luca Foresti, Lauro Snidaro · 2003
Multi-camera firsion is rapidly becoming an emerging research area, especially for visual surveillance applications. Data fusion can be obtained with calibrated cameras, either calibrating prior use - following standard techniques (I) - or through learning mechanism in 30 Cartesian frame (2), typically the scene ground plane. In this paper we describe a method to merge video data acquired by two overlapping views, by learning the camera registration on the basis of occurring dynamics. Scene dynamics in each independent view can be modeled as a mixture of Gaussian components, and that dynamics can be coupled assuming stochastic correlation between the underlying processes.