An artificial neural network for motion detection and speed estimation

An artificial neural network with nonlinear spatiotemporal features performing motion detection (i.e. speed estimation and direction selection) is introduced. The input to the network is a moving visual pattern, and the output is the direction of motion (directional selectivity) and an estimate of the velocity magnitude (speed estimation). To carry out the process, the network utilizes three layers. The first layer performs spatiotemporal processing using a difference-of-Gaussians spatial filter and a bandpass differentiating temporal filter. In the second layer, nonlinear spatiotemporal operations extract features pertinent to the estimation of speed-in the form of spikelike trains-and directional selectivity. The third layer provides connections and nodes where directional selectivity is implemented, and an estimate of the speed is formed by temporally processing the spikelike trains provided by the second layer. Computer simulations of a one-dimensional application of the network are presented, illustrating the responses to moving spots and edges with a number of velocity profiles. The fundamental limitations on the proposed network are explored as well