Unscented Kalman filter for visual curve tracking
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摘要
Visual contour tracking in complex background is a difficult task. The measurement model is often nonlinear due to clutter in images. Traditional visual trackers based on Kalman filters employ simple linear measurement models, and often collapse during the tracking process. This paper presents a new contour tracker based on unscented Kalman filter that is superior to extended Kalman filter both in theory and in many practical situations. The new tracker employs a more accurate nonlinear measurement model, without computation of a Jacobian matrix. During each time step, the tracker makes multiple measurements in terms of the set of appropriately chosen sample points, thus obtaining the best observation according to the measurement density. The resulting algorithm is able to obtain a more exact estimate of the state of the system, while having the same order of complexity as that of an extend Kalman Filter. The experiments show that the new algorithm is superior to those based on Kalman filters.
论文关键词:Visual tracking,Unscented Kalman filter,Kalman filter
论文评审过程:Received 26 September 2002, Revised 27 June 2003, Accepted 2 July 2003, Available online 19 September 2003.
论文官网地址:https://doi.org/10.1016/j.imavis.2003.07.004