Motion detection by a moving observer using Kalman filter and neuralnetwork in soccer robot

In many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. Duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. In thispaper, we propose a novel method to effectively overcome this problem using Neural Networks and Kalman Filtering theory. Thistechnique uses movement parameters of camera to resolve problems caused by error in image processing outputs. The technique issuccessfully applied in the MRL Middle Size Soccer Robots where ball motion detection has an especial importance in their decisionmaking. Experimental results are presented and 2.2% achieved error suggests that the combined approach performs significantly better thantraditional techniques.