As the cost of computing per-pixel depth imagery from stereo cameras in real time has fallen rapidly in recent years, interest in using stereo vision for person tracking has greatly increased. Methods that attempt to track people directly in these "camera- view" depth images are confronted by their substantial amounts of noise and unreliable data. Some recent methods have therefore found it useful to first compute overhead, "plan-view" statistics of the depth data, and then track people in images of these statistics. We describe a new combination of plan-view statistics that better represents the shape of tracked objects and provides a more robust substrate for person detection and tracking than prior plan-view algorithms. We also introduce a new method of plan- view person tracking, using adaptive statistical templates and Kalman prediction. Adaptive templates provide more detailed models of tracked objects than prior choices such as Gaussians, and we illustrate that the typical problems with template-based tracking in camera-view images are easily avoided in a plan- view framework. We compare results of our method with those for techniques using different plan-view statistics or person models, and find our method to exhibit superior tracking through challenging phenomena such as complex inter-person occlusions and close interactions. Reasonable values for most system parameters may be derived from physically measurable quantities such as average person dimensions. Notes: Copyright Elsevier B.V. To be published in the Journal of Image and Vision Computing, 2003. 31 Pages
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