Compensation of abrupt motion changes in target tracking by visual servoing

Abstract

This paper describes a real time visual target tracking using the generalized likelihood ratio (GLR) algorithm. The authors' first introduce the visual servoing approach and the application of the task function concept to vision-based tasks. Then, the authors present a complete control scheme which explicitly enables a moving object to be pursued. In order to make the tracking errors as low as possible, the authors use the GLR test, an algorithm able to detect, estimate and compensate abrupt jumps in target motion. Finally, real-time experimental results using a camera mounted on the end effector of a six-DOF robot are presented.