The extended co-learning framework for robust object tracking

Gong, C; Liu, Y; Li, T; Yang, J; He, X

The extended co-learning framework for robust object tracking

Gong, C Liu, Y Li, T Yang, J He, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2012, pp. 398 - 403
Issue Date:: 2012-11-05

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Field	Value	Language
dc.contributor.author	Gong, C	en_US
dc.contributor.author	Liu, Y	en_US
dc.contributor.author	Li, T	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2012-11-05	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2012, pp. 398 - 403	en_US
dc.identifier.issn	1945-7871	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22958
dc.description.abstract	Recently, object tracking has been widely studied as a binary classification problem. Semi-supervised learning is particularly suitable for improving classification accuracy when large quantities of unlabeled samples are generated (just like tracking procedure). The purpose of this paper is to fulfill robust and stable tracking by using collaborative learning, which belongs to the scope of semi-supervised learning, among three classifiers. Different from [1], random fern classifier is incorporated to deal with 2bitBP feature newly added and certain constraints are specially implemented in our framework. Besides, the way for selecting positive samples is also altered by us in order to achieve more stable tracking. Algorithm proposed in this paper is validated by tracking pedestrian and cup under occlusion. Experiments and comparison show that our algorithm can avoid drifting problem to some degree and make tracking result more robust and adaptive. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo	en_US
dc.relation.isbasedon	10.1109/ICME.2012.181	en_US
dc.title	The extended co-learning framework for robust object tracking	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia	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 - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Recently, object tracking has been widely studied as a binary classification problem. Semi-supervised learning is particularly suitable for improving classification accuracy when large quantities of unlabeled samples are generated (just like tracking procedure). The purpose of this paper is to fulfill robust and stable tracking by using collaborative learning, which belongs to the scope of semi-supervised learning, among three classifiers. Different from [1], random fern classifier is incorporated to deal with 2bitBP feature newly added and certain constraints are specially implemented in our framework. Besides, the way for selecting positive samples is also altered by us in order to achieve more stable tracking. Algorithm proposed in this paper is validated by tracking pedestrian and cup under occlusion. Experiments and comparison show that our algorithm can avoid drifting problem to some degree and make tracking result more robust and adaptive. © 2012 IEEE.

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http://hdl.handle.net/10453/22958