A robust people detection, tracking, and counting system

Kirchner, N; Alempijevic, A; Virgona, A; Dai, X; Plöger, PG; Venkat, RK

A robust people detection, tracking, and counting system

Kirchner, N

Alempijevic, A

Virgona, A

Dai, X Plöger, PG Venkat, RK

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2014, 02-04-December-2014
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Kirchner, N https://orcid.org/0000-0002-3018-992X	en_US
dc.contributor.author	Alempijevic, A https://orcid.org/0000-0002-1769-8041	en_US
dc.contributor.author	Virgona, A https://orcid.org/0000-0002-9315-7431	en_US
dc.contributor.author	Dai, X	en_US
dc.contributor.author	Plöger, PG	en_US
dc.contributor.author	Venkat, RK	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2014, 02-04-December-2014	en_US
dc.identifier.isbn	9780980740448	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/30687
dc.description.abstract	The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed. This paper presents our approach for robust real world people detection, tracking and counting using a PrimeSense RGBD camera. Our past research, upon which we built, is highlighted and novel methods to solve the problems of sensor self-localisation, false negatives due to persons physically interacting with the environment, and track misassociation due to crowdedness are presented. An empirical evaluation of our approach in a major Sydney public train station (N=420) was conducted, and results demonstrating our methods in the complexities of this challenging environment are presented.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.title	A robust people detection, tracking, and counting system	en_US
dc.type	Conference Proceeding
utslib.citation.volume	02-04-December-2014	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Melbourne, Australia
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
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	open_access
pubs.publication-status	Published	en_US
pubs.volume	02-04-December-2014	en_US

Abstract:

The ability to track moving people is a key aspect of autonomous robot systems in real-world environments. Whilst for many tasks knowing the approximate positions of people may be sufficient, the ability to identify unique people is needed to accurately count people in the real world. To accomplish the people counting task, a robust system for people detection, tracking and identification is needed. This paper presents our approach for robust real world people detection, tracking and counting using a PrimeSense RGBD camera. Our past research, upon which we built, is highlighted and novel methods to solve the problems of sensor self-localisation, false negatives due to persons physically interacting with the environment, and track misassociation due to crowdedness are presented. An empirical evaluation of our approach in a major Sydney public train station (N=420) was conducted, and results demonstrating our methods in the complexities of this challenging environment are presented.

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http://hdl.handle.net/10453/30687