Detecting Major Segmentation Errors for a Tracked Person Using Colour Feature Analysis

Madden, CS; Piccardi, M

Detecting Major Segmentation Errors for a Tracked Person Using Colour Feature Analysis

Madden, CS Piccardi, M

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Publisher:: IEEE Computer Society
Publication Type:: Conference Proceeding
Citation:: Proceedings of 14th International Conference on Image Analysis and Processing 2007, 2007, pp. 524 - 529
Issue Date:: 2007-01

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Field	Value	Language
dc.contributor.author	Madden, CS	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.contributor.editor	Prati, A	en_US
dc.date	2007-09-10	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	Proceedings of 14th International Conference on Image Analysis and Processing 2007, 2007, pp. 524 - 529	en_US
dc.identifier.isbn	0-7695-2877-5	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2475
dc.description.abstract	This paper presents a method to identify frames with significant segmentation errors in an individuals track by analysing the changes in appearance and size features along the frame sequence. The features used and compared include global colour histograms, local histograms and the bounding box size. Experiments were carried out on 26 tracks from 4 different people across two cameras with differing illumination conditions. By fusing two local colour features with a global colour feature, probabilities of segmentation error detection as high as 83 percent of human expert-identified major segmentation errors are achieved with false alarm rates of only 3 percent. This indicates that the analysis of such features along a track can be useful in the automatic detection of significant segmentation errors. This can improve the final results of many applications that wish to use robust segmentation results from a tracked person.	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0452657
dc.relation.ispartof	Proceedings of 14th International Conference on Image Analysis and Processing 2007	en_US
dc.relation.ispartof	International Conference on Image Analysis and Processing	en_US
dc.relation.isbasedon	10.1109/ICIAP.2007.51	en_US
dc.rights	© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Detecting Major Segmentation Errors for a Tracked Person Using Colour Feature Analysis	en_US
dc.type	Conference Proceeding
utslib.location	Los Alamitos, CA, USA	en_US
utslib.location.activity	Modena, Italy	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Modena, Italy	en_US
dc.location.activity	Hyderabad, INDIA
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	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Los Alamitos, CA, USA	en_US
pubs.start-date	2007-09-10	en_US

Abstract:

This paper presents a method to identify frames with significant segmentation errors in an individuals track by analysing the changes in appearance and size features along the frame sequence. The features used and compared include global colour histograms, local histograms and the bounding box size. Experiments were carried out on 26 tracks from 4 different people across two cameras with differing illumination conditions. By fusing two local colour features with a global colour feature, probabilities of segmentation error detection as high as 83 percent of human expert-identified major segmentation errors are achieved with false alarm rates of only 3 percent. This indicates that the analysis of such features along a track can be useful in the automatic detection of significant segmentation errors. This can improve the final results of many applications that wish to use robust segmentation results from a tracked person.

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http://hdl.handle.net/10453/2475