1st IEEE Workshop on Performance Evaluation of Tracking and Surveillance (PETS2000)

Tracking without Feature Detection

Arthur Pece and Anthony Worrall

Computational Vision Group, Department of Computer Science, The University of Reading, UK.

The tracking method presented in this paper is based on a pose-refinement algorithm which is a variant of active contours. The most important difference between our method and most active contour methods is that no "features" are detected at any stage: the evaluation function for the pose parameters is based on all the grey-level information extracted from the normals to the model contours, without thresholding. One advantage of the feature-free evaluation function is that it is a smooth function of both the pose parameters and the image grey levels. Another advantage is that it leads to a covariance estimate for the Bayesian evidence for the pose parameters. This covariance estimate is used for efficient pose optimisation by a Newton-like method and for proper weighting of the innovation in a Kalman filter. The method is demonstrated by tracking cars with 3-D models.

Last Modified 3 March 2000 18:46