Improving the robustness of parametric shape tracking with switched multiple models ?
This paper addresses the problem of tracking objects with complex motion dynamics or shape changes. It is assumed that some of the visual features detected in the image (e.g., edge strokes) are outliers i.e., they do not belong to the object boundary. A robust tracking algorithm is proposed which allows to efficiently track an object with complex shape or motion changes in clutter environments. The algorithm relies on the use of multiple models, i.e., a bank of stochastic motion models switched according to a probabilistic mechanism. Robust filtering methods are used to estimate the label of the active model as well as the state trajectory.
strokes outliers robust multi-model tracker (rmm) kalman multi-model tracker (kmm) dynamic models gaussian