A method for optical flow estimation from an image sequence using a neural network is presented. Under hypothesis based on local rigidity, translational motion and smoothness constraints, a neural network is designed to estimate the optical flow. Experimental results using real world I.R. images are presented to demonstrate the efficiency of this method compared to Horn and Schunck algorithm.
Roger Samy, Roger Samy,
"Neural Network For Optical Flow Estimation", Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969757; https://doi.org/10.1117/12.969757