Optical Filtering Approach to Regularized Tracking of an Object's Position and Orientation

We present a regularized nonlinear least-squares algorithm for tracking the position and the orientation of a known object by using an active camera and an optical correlator situated between digital preprocessing and postprocessing operations. The numerical minimization required by the regularized least-squares solution is implemented by use of a rapid look-up table method. Performance of the algorithm is evaluated through a Monte Carlo sensitivity analysis that incorporates models for lens blur, image noise, illumination variation, and partial occlusion. This analysis shows robust performance with respect to image noise, partial occlusion of the object, and errors in the camera pan and tilt used to follow the moving object. The limiting factors in the algorithm's performance are errors in the preprocessing step used to scale and rotate the input video images. These errors should be maintained within 6% and 3 degrees , respectively.