Visual tracking is an old area that has recently seen a surge in activity. The interest has been fueled by progress in related fields like detection, segmentation and optic flow as well as by application-driven demand and the increase in the available computing power.

The published tracking methods differ in many aspects such as the speed, the complexity of the model of the tracked entity, the (geometric) transformations assumed, the mode of operation (casual and non-causal), the ability to adapt and learn, the robustness to occlusion and assumptions about the observer. I will review the dataset used in recent publications and show that the "tracker space" is still wide open with large areas to be explored.

I will then present three trackers developed by me and my collaborators that operate at very different points in the speed-robustness-flexibility space that are close to the "convex hull" of published methods: the TLD tracker, the Flock-of-Trackers and the Zero-Shift-Point tracker. I will focus on a common aspect shared by the trackers: mechanisms for prediction and handling of tracking errors. Such mechanisms contribute to tracker robustness, which will be demonstrated live.