Robust Local Optical Flow for Feature Tracking

This paper is motivated by the problem of local motion estimation via robust regression with linear models. In order to increase the robustness of the motion estimates, we propose a novel robust local optical flow approach based on a modified Hampel estimator. We show the deficiencies of the least squares estimator used by the standard Kanade-Lucas-Tomasi (KLT) tracker when the assumptions made by Lucas-Kanade are violated. We propose a strategy to adapt the window sizes to cope with the generalized aperture problem. Finally, we evaluate our method on the Middlebury and MIT dataset and show that the algorithm provides excellent feature tracking performance with only slightly increased computational complexity compared to KLT. To facilitate further development, the presented algorithm can be downloaded from http://www.nue.tu-berlin.de/menue/forschung/projekte/rlof.