Multi-step active object tracking with entropy based optimal actions using the sequential Kalman filter

We describe an enhanced method for the selection of optimal sensor actions in a probabilistic state estimation framework. We apply this to the selection of optimal focal lengths for cameras with a variable motor zoom in a real-time visual object tracking task. The optimal camera action is determined by the expected state estimate entropy for each candidate action. Varying action costs are taken into account by predicting the entropy several steps into the future. Our contribution is the use of the sequential Kalman filter to deal transparently with a variable number of cameras, potential object loss in a subset of the cameras, and to reduce the calculation time through independent optimization.