A Neural Controller based Binocular Vision System for Object Tracking

Abstract

In this paper, we present a learning control approach to the problem of visual tracking using an active binocular vision system. The simulated vision system used here is composed of two cameras which have freedom of pan and are mounted on a plate that can tilt. A self organizing map is trained to learn the mapping between visual feedback as received from the simulated CCD cameras and the incremental values in the orientation angles of the vision system to fixate continuously on a mobile target. A direct implication of this is that the object should be centered at the vergence point of the cameras. This is accomplished by ensuring minimum disparity of the target vector from the center of the images of both the cameras. The learning algorithm is presented and the simulation study shows an average focussing error of 0.15 pixels after 1200 iterations for a small, defined joint angle space of the vision system. Thus, a significant contribution of this work is the application of a self organizing map in dynamic object tracking.