Mutual information based data selection in Gaussian Processes for 2D laser range finder based people tracking

Zainudin, Z; Kodagoda, S; Dissanayake, G

Mutual information based data selection in Gaussian Processes for 2D laser range finder based people tracking

Zainudin, Z Kodagoda, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013, 2013, pp. 477 - 482
Issue Date:: 2013-09-16

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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	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2013-09-16	en_US
dc.identifier.citation	2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013, 2013, pp. 477 - 482	en_US
dc.identifier.isbn	9781467353199	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23612
dc.description.abstract	In general, a model to describe human motion patterns should have a capability of enhancing tracking performance even with long term occlusions. One way of effectively learn these patterns is to apply Gaussian Processes (GP). However, with the increase of the amount of training data with time, the GP becomes computationally expensive. 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 90% data reduction while keeping the limit of Average Route Mean Square Error (ARMSE). We have also implemented a GP based Particle filter tracker for long term people tracking with occlusions. The comparison results with Extended Kalman Filter (EKF) based tracker shows the superiority of the proposed approach. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing, AIM 2013	en_US
dc.relation.isbasedon	10.1109/AIM.2013.6584137	en_US
dc.title	Mutual information based data selection in Gaussian Processes for 2D laser range finder based people tracking	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Wollongong, Australia	en_US
dc.location.activity	Wollongong, AUSTRALIA
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
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:

In general, a model to describe human motion patterns should have a capability of enhancing tracking performance even with long term occlusions. One way of effectively learn these patterns is to apply Gaussian Processes (GP). However, with the increase of the amount of training data with time, the GP becomes computationally expensive. 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 90% data reduction while keeping the limit of Average Route Mean Square Error (ARMSE). We have also implemented a GP based Particle filter tracker for long term people tracking with occlusions. The comparison results with Extended Kalman Filter (EKF) based tracker shows the superiority of the proposed approach. © 2013 IEEE.

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http://hdl.handle.net/10453/23612