Exploiting Semantic Information and Deep Matching for Optical Flow [chapter]

Min Bai, Wenjie Luo, Kaustav Kundu, Raquel Urtasun
2016 Lecture Notes in Computer Science  
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well as a relatively small number of traffic participants which move rigidly in 3D. We propose to estimate the traffic participants using instance-level segmentation. For each traffic participant, we use the epipolar constraints that govern each independent motion for faster and more accurate
more » ... imation. Our second contribution is a new convolutional net that learns to perform flow matching, and is able to estimate the uncertainty of its matches. This is a core element of our flow estimation pipeline. We demonstrate the effectiveness of our approach in the challenging KITTI 2015 flow benchmark, and show that our approach outperforms published approaches by a large margin.
doi:10.1007/978-3-319-46466-4_10 fatcat:zmpui24d3bbv7n7pvdnowjj4aq