Mutual information based data selection in Gaussian processes for people tracking

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Conference Proceeding
Australasian Conference on Robotics and Automation, ACRA, 2012
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It is the general perception that models describing human motion patterns enhance tracking even with long term occlusions. One effective way of learning such patterns is to use Gaussian Processes (GP). However, with the increase of the amount of training data with time, the GP becomes computationally intractable. In this work, we have proposed a Mutual Information (MI) based technique along with the Mahalanobis Distance (MD) measure to keep the most informative data while discarding the least informative data. The algorithm is tested with data collected in an office environment with a Segway robot equipped with a laser range finder. It leads to more than 80% data reduction while keeping the rms errors within the required bounds. We have also implemented a GP based Particle filter tracker for long term people tracking with occlusions. The comparison results with Extended Kalman Filter based tracker shows the superiority of the proposed approach.
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