KALMAN FILTER AND NARX NEURAL NETWORK FOR ROBOT VISION BASED HUMAN TRACKING

Abstract

Tracking human is an important and challenging problem in video-based intelligent robot systems. In this paper, a vision-based human tracking system is supposed to provide sensor input for vision-based control of a mobile robot that works in a team helping the human co-worker. A comparison between NARX neural network and Kalman filter in solving the prediction problem of human tracking in robot vision is presented. After collecting video data from a robot, simulation results obtained from the Kalman filter model are used to compare with the simulation results obtained from the NARX Neural network.Key words: robot vision, Kalman filter, neural networks, human trackin