Video motion estimation using a neural network

This paper presents a novel technique for motion estimation in video frame sequences. It uses a modified Hopfield neural network. The procedure consists of two stages: estimation of the neural network parameters from the present and past frames or subimages, followed by estimation of the motion vector. The latter utilizes a dynamic iterative algorithm to minimize the energy function of the neural network. Due to the neural network´s fault-tolerant nature and parallel computation capability, fast, accurate, and reliable results are obtained. The usefulness and accuracy of the approach is demonstrated upon both synthetic and real images