A self-organizing video neuro-tracker

We investigate a radial basis function neural network for the control of a video camera for tracking a target moving at high speed. The adjusting of the weights and parameters of the network minimizes the performance measureP that is the sum of the squared errors between the target position on the screen and the center of the screen over an entire trajectory of the target. Every adjustment of a single weight requires the evaluation of the performance measure over the fixed target trajectory. A very general target trajectory is required for generalized training of the network to appropriately control any camera and target situation. The results show considerable promise for this and similar cases where a trainable controller is desired. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996.