# Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information

arXiv: https://arxiv.org/abs/1907.06989 · HTML: https://mlsanity.com/paper/1907.06989/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention](https://mlsanity.com/paper/2212.05432/) (2022)

## Abstract (arXiv)

In this technical report we investigate speed estimation of the ego-vehicle on the KITTI benchmark using state-of-the-art deep neural network based optical flow and single-view depth prediction methods. Using a straightforward intuitive approach and approximating a single scale factor, we evaluate several application schemes of the deep networks and formulate meaningful conclusions 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 methods influences speed estimation performance; using the depth and optical flow results from smaller crops of wide images degrades performance. With these observations in mind, we achieve a RMSE of less than 1 m/s for vehicle speed estimation using monocular images as input from recordings of the KITTI benchmark. Limitations and possible future directions are discussed as well.