  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.
