Learning to track with multiple observers

We propose a novel approach to designing algorithms for object tracking based on fusing multiple observation models. As the space of possible observation models is too large for exhaustive on-line search, this work aims to select models that are suitable for a particular tracking task at hand. During an off-line training stage observation models from various off-the-shelf trackers are evaluated. From this data different methods of fusing the observers on-line are investigated, including parallel and cascaded evaluation. Experiments on test sequences show that this evaluation is useful for automatically designing and assessing algorithms for a particular tracking task. Results are shown for face tracking with a handheld camera and hand tracking for gesture interaction. We show that for these cases combining a small number of observers in a sequential cascade results in efficient algorithms that are both robust and precise.