Karel Zimmermann, Tomáš Svoboda, and Jirí Matas. Adaptive parameter optimization for real-time tracking. In Proceedings of 11th IEEE International Conference on Computer Vision, workshop on Non-rigid registration and tracking through learning, page 8, Madison, USA, October 2007. Omnipress.
Adaptation of a tracking procedure combined in a common way with a Kalman filter is formulated as an constrained optimization problem, where a trade-off between precision and loss-of-lock probability is explicitly taken into account. While the tracker is learned in order to minimize computational complexity during a learning stage, in a tracking stage the precision is maximized online under a constraint imposed by the loss-of-lock probability resulting in an optimal setting of the tracking procedure. We experimentally show that the proposed method converges to a steady solution in all variables. In contrast to a common Kalman filter based tracking, we achieve a significantly lower state covariance matrix. We also show, that if the covariance matrix is continuously updated, the method is able to adapt to a different situations. If a dynamic model is precise enough the tracker is allowed to spend a longer time with a fine motion estimation, however, if the motion gets saccadic, i.e. unpredictable by the dynamic model, the method automatically gives up the precision in order to avoid loss-of-lock.
