ESTIMATING MOVEMENT DIRECTION WITH A NEURAL NETWORK

Various schemes have been developed to identify the direction of motion in a scene, often to assist in compressing the information required to broadcast a sequence of images. These schemes typically make sequential comparisons of blocks of pixels in order to arrive at a most likely direction of motion. This paper investigates how an artificial neural network may be used to perform the same task. The approach is interesting because these networks perform computations in parallel, thus allowing a form of top-down as well as bottom-up processing. Also, because computations are conceptually performed in parallel, it would be possible to consider performing the task in real time with an appropriate hardware implementation. Preliminary results show that a properly trained network has interesting properties similar to those of real neurons and can, indeed, report direction of movement based on binary pixel values…