A Neural Network for Egomotion Estimation from Optical Flow.

In this work we consider the problem to determine qualitative information about the motion of a viewer moving in a stationary environment. First the optical flow (OF) is computed using a token based approach estimating the 2D velocity vectors only for some interesting points. Then our method estimates the motion of the viewer using only the available sparse OF. A neural network extracts information about stable points useful for the computation of vehicle's heading and Time-to-Collision (TTC). A number of experiments showing the efficacy and robustness of the method have been performed both on synthetic image sequences and on real images acquired by a CCD camera mounted on a mobile platform.