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arXiv · 1604.01827

Exploiting Semantic Information and Deep Matching for Optical Flow

Min Bai·Wenjie Luo·Kaustav Kundu·Raquel Urtasun
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Abstract

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 estimation. 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.

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Min Bai, Wenjie Luo, Kaustav Kundu, Raquel Urtasun. 2016-04-06. Exploiting Semantic Information and Deep Matching for Optical Flow. https://arxiv.org/abs/1604.01827

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