System-wide tracking of individuals

Madden, C; Piccardi, M

System-wide tracking of individuals

Madden, C Piccardi, M

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Publication Type:: Chapter
Citation:: 2011, 336 pp. 83 - 103
Issue Date:: 2011-03-07

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Field	Value	Language
dc.contributor.author	Madden, C	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2011-03-07	en_US
dc.identifier.citation	2011, 336 pp. 83 - 103	en_US
dc.identifier.isbn	9783642182778	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17750
dc.description.abstract	Tracking the movements of people within large video surveillance systems is becoming increasingly important in the current security conscious environment. Such system-wide tracking is based on algorithms for tracking a person within a single camera, which typically operate by extracting features that describe the shape, appearance and motion of that person as they are observed in each video frame. These features can be extracted then matched across different cameras to obtain global tracks that span multiple cameras within the surveillance area. In this chapter, we combine a number of such features within a statistical framework to determine the probability of any two tracks being made by the same individual. Techniques are presented to improve the accuracy of the features. These include the application of spatial or temporal smoothing, the identification and removal of significant feature errors, as well as the mitigation of other potential error sources, such as illumination. The results of tracking using individual features and the combined system-wide tracks are presented based upon an analysis of people observed in real surveillance footage. These show that software operating on current camera technology can provide significant assistance to security operators in the system-wide tracking of individual people. © 2011 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-642-18278-5_5	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	System-wide tracking of individuals	en_US
dc.type	Chapter
utslib.citation.volume	336	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	336	en_US

Abstract:

Tracking the movements of people within large video surveillance systems is becoming increasingly important in the current security conscious environment. Such system-wide tracking is based on algorithms for tracking a person within a single camera, which typically operate by extracting features that describe the shape, appearance and motion of that person as they are observed in each video frame. These features can be extracted then matched across different cameras to obtain global tracks that span multiple cameras within the surveillance area. In this chapter, we combine a number of such features within a statistical framework to determine the probability of any two tracks being made by the same individual. Techniques are presented to improve the accuracy of the features. These include the application of spatial or temporal smoothing, the identification and removal of significant feature errors, as well as the mitigation of other potential error sources, such as illumination. The results of tracking using individual features and the combined system-wide tracks are presented based upon an analysis of people observed in real surveillance footage. These show that software operating on current camera technology can provide significant assistance to security operators in the system-wide tracking of individual people. © 2011 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/17750