We propose a learning-based approach for motion boundary detection. Precise localization of motion bound aries is essential for the success of optical flow estimation, as motion boundaries correspond to discontinuities of the optical flow field. The proposed approach allows to pre dict motion boundaries, using a structured random forest trained on the ground-truth of the MPI-Sintel dataset. The random forest leverages several cues at the patch level, namely appearance (RGB color) and motion cues (optical flow estimated by state-of-the-art algorithms). Experimen tal results show that the proposed approach is both robust and computationally efficient. It significantly outpeiforms state-of-the-art motion-dif f erence approaches on the MPI Sintel and Middlebury datasets. We compare the results ob tained with several state-of-the-art optical flow approaches and study the impact of the dif f erent cues used in the ran dom forest. Furthermore, we introduce a new dataset, the YouTube Motion Boundaries dataset (YMB), that com prises 60 sequences taken from real-world videos with man ually annotated motion boundaries. On this dataset, our ap proach, although trained on MPI-Sintel, also outpeiforms by a large margin state-of-the-art optical flow algorithms.
Weinzaepfel et al. REF use a structured random forest trained on appearance and motion cues.
Learning to detect Motion Boundaries
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science