Performance assessment of model-based tracking

Abstract

Model-based vision techniques, originally developed for the recognition and pose recovery of vehicles in a single image, are used here to track vehicles through a sequence of images. Knowledge of the position of the camera with respect to the ground plane is used to reduce the search space of possible vehicle positions from six dimensions to three. The expected dynamics of vehicles are expressed in a Kalman filter, which predicts the likely poses in successive frames and provides a smoothed description of the vehicles' motion. The notion of equivalence classes defined by a search of the parameter space is developed as an indicator of the performance of the pose-refinement sub-system. The system is illustrated and assessed by using the size of the correct class as a performance measure.