Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information
This is an incremental improvement for autonomous driving systems using monocular cameras.
The paper tackled vehicle speed estimation from monocular images on the KITTI benchmark by combining optical flow and depth information from deep neural networks, achieving an RMSE of less than 1 m/s.
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.