Mutual information based data selection in Gaussian processes for people tracking

Zainudin, Z; Kodagoda, S; Van Nguyen, L

Mutual information based data selection in Gaussian processes for people tracking

Zainudin, Z Kodagoda, S

Van Nguyen, L

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2012
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Zainudin, Z	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Van Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2012	en_US
dc.identifier.isbn	9780980740431	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23113
dc.description.abstract	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.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.title	Mutual information based data selection in Gaussian processes for people tracking	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	080104 Computer Vision	en_US
dc.location.activity	Wellington, New Zealand	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

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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http://hdl.handle.net/10453/23113