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Abstract. The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation.

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Since two years there is a recent trend in optical flow estimation to improve the results of state-of-the-art variational methods by applying additional filtering steps such as median filters, bilateral filters, and non-local techniques. So far, however, the application of such filters has been restricted to two-frame optical flow methods. In this paper, we go beyond this two-frame case and investigate the usefulness of such filtering steps for multi-frame optical flow estimation. Thereby we consider both the application to single flow fields as well as the filtering of the entire spatio-temporal flow volume. In this context, we propose the use of a joint trilateral filter that processes all flow fields simultaneously while imposing consistency of joint flow structures at the same time. Evaluations on the Middlebury benchmark clearly demonstrate the success of our filtering strategy. Achieving rank 3, our method yields state-of-the art results and significantly outperforms the baseline method providing considerably sharper results.