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Proceedings IEEE Workshop on Applications of Computer Vision (1992)
Palm Springs, CA, USA
Nov. 30, 1992 to Dec. 2, 1992
ISBN: 0-8186-2840-5
pp: 28-35
K.D. Baker , Dept. of Comput. Sci., Reading Univ., UK
G.D. Sullivan , Dept. of Comput. Sci., Reading Univ., UK
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.<>
INDEX TERMS
computer vision, equivalence classes, Kalman filters, traffic recording
CITATION

K. Baker and G. Sullivan, "Performance assessment of model-based tracking," Proceedings IEEE Workshop on Applications of Computer Vision(ACV), Palm Springs, CA, USA, , pp. 28-35.
doi:10.1109/ACV.1992.240330
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