Intuitive Estimation of Speed using Motion and Monocular Depth Information

  • R.A. Rill Faculty of Informatics, Eötvös Loránd University. H-1117 Budapest, ´ Pázmány P. stny 1/C, Hungary and Faculty of Mathematics and Computer Science, Babeș-Bolyai University, No. 1 Mihail Kogalniceanu St., RO-400084 Cluj-Napoca, Romania.


Advances in deep learning make monocular vision approaches attractive for the autonomous driving domain. This work investigates a method for estimating the speed of the ego-vehicle using state-of-the-art deep neural network based optical flow and single-view depth prediction models. Adopting a straightforward intuitive approach and approximating a single scale factor, several application schemes of the deep networks are evaluated and meaningful conclusions are formulated, such as: combining depth information with optical flow improves speed estimation accuracy as opposed to using optical flow alone; the quality of the deep neural network results influences speed estimation performance; using the depth and optical flow data from smaller crops of wide images degrades performance. With these observations in mind, a RMSE of less than 1 m/s for ego-speed estimation was achieved on the KITTI benchmark using monocular images as input. Limitations and possible future directions are discussed as well.


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How to Cite
RILL, R.A.. Intuitive Estimation of Speed using Motion and Monocular Depth Information. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 65, n. 1, p. 33-45, apr. 2020. ISSN 2065-9601. Available at: <>. Date accessed: 12 june 2024. doi: