FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking

In the domain of video tracking, existing methods often grapple with a trade-off between spatial density and temporal range. Current approaches in dense optical flow estimators excel in providing spatially dense tracking but are limited to short temporal spans. Conversely, recent advancements in long-range trackers offer extended temporal coverage but at the cost of spatial sparsity. This paper introduces FlowTrack, a novel framework designed to bridge this gap. FlowTrack combines the strengths of both paradigms by 1) chaining confident flow predictions to maximize efficiency and 2) automatically switching to an error compensation module in instances of flow prediction inaccuracies. This dual strategy not only offers efficient dense tracking over extended temporal spans but also ensures robustness against error accumulations and occlusions, common pitfalls of naive flow chaining. Furthermore, we demonstrate that chained flow itself can serve as an effective guide for an error compensation module, even for occluded points. Our framework achieves state-of-the-art accuracy for longrange tracking on the DAVIS dataset, and renders 50% speed-up when performing dense tracking.