We propose that better tracking can be achieved by learning to automatically associate different videos (or parts) with different algorithms. Instead of seeking an elusive one-size-fits-all tracking strategy (often in the form of an energy function), we advocate keeping multiple strategies, but recognizing when/where to use each. We demonstrate this approach for the problems of optical flow and interest-point tracking.
This is work done jointly with Oisin Mac Aodha, Cristina Garcia Cifuentes, and Ahmad Humayun.