"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. unpr" . "Adaptive parameter optimization for real-time tracking" . "Adaptive parameter optimization for real-time tracking"@en . . "978-1-4244-1630-1" . "RIV/68407700:21230/07:03135628" . "Madison" . "3"^^ . "NRTL 2007: Proceedings of workshop on Non-rigid registration and tracking through learning - ICCV" . "Adaptive parameter optimization for real-time tracking" . . "8"^^ . . . "408541" . . . "Matas, Ji\u0159\u00ED" . . "[D2CBDB39B821]" . "motion estimation; real-time; tracking"@en . "Adaptive parameter optimization for real-time tracking"@cs . "Ne\u010D\u00EDslov\u00E1no" . . . . "Svoboda, Tom\u00E1\u0161" . . . "Zimmermann, Karel" . . "21230" . "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. unpr"@cs . . . "2007-10-14+02:00"^^ . "RIV/68407700:21230/07:03135628!RIV08-GA0-21230___" . . "Rio de Janeiro" . "P(1ET101210407), P(GA102/07/1317)" . . "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. unpr"@en . "Adaptive parameter optimization for real-time tracking"@cs . "3"^^ . . "Adaptive parameter optimization for real-time tracking"@en .