Robust probabilistic optical flow for video sequences

The optical flow estimation is addressed in the context of video sequences, where temporal information can be exploited to increase the accuracy and the convergence speed of the algorithm. This paper presents an unsupervised optical flow algorithm based on robust Student's t data and regularization terms, which automatically tunes the relative weight of the data adequacy and regularization terms. The contribution of this paper is twofold. Firstly, it gives a more tractable and fully parallel formulation of the aforementioned algorithm, which significantly enhances the speed performances, and, secondly, it exploits the temporal smoothness information by introducing spatio-temporal regularization.