Robotics: Science and Systems Conference - A Probabilistic Exemplar Approach to Combine Laser and Vision for Person Tracking

Author: D, Schulz

Abstract:
This article presents an approach to person tracking that combines camera images and laser range data. The method uses probabilistic exemplar models, which represent typical appearances of persons in the sensor data by metric mixture distributions. Our approach learns such models for laser and for camera data and applies a Rao-Blackwellized particle filter in order to track a persons appearance in the data. The filter samples joint exemplar states and tracks the persons position conditioned on the exemplar states using a Kalman filter. We describe an implementation of the approach based on contours in images and laser point set features. Additionally, we show how the models can be learned from training data using clustering and EM. Finally, we give first experimental results of the method which show that it is superior to purely laser-based approaches for determining the position of persons in images.

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