Robust Optical Flow Estimation For Continuous Blurred Scenes Using RGB-Motion Imaging And Directional Filtering

Optical flow estimation is a difficult task given real-world video footage with camera and object blur. In this paper we combine a 3D pose&position tracker with an RUB sensor allowing us to capture video footage together with 3D camera motion. We show that the additional camera motion information can be embedded into a hybrid optical flow framework by interleaving an iterative blind deconvolution and warping based minimization scheme. Such a hybrid framework significantly improves the accuracy of optical flow estimation in scenes with strong blur Our approach yields improved overall petformance against three state-of-the-art baseline methods applied to our proposed ground truth sequences, as well as in several other real-world sequences captured by our novel imaging system.