Learning to track: conceptual manifold map for closed-form tracking

Our objective is to model the visual manifold of object appearance corresponding to geometric transformation. We learn a generative model for object appearance where the appearance of the object at each new frame is a function that maps from a conceptual representation of the geometric transformation space into the visual manifold. By learning such generative model we can infer the geometric transformation (track) directly from the tracked object appearance. As a result tracking can be achieved in a closed form and therefore can be done very efficiently.