Deep learning for robust outdoor vehicle visual tracking

Xin, JN; Du, X; Zhang, J

Deep learning for robust outdoor vehicle visual tracking

Xin, JN Du, X Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2017, pp. 613 - 618
Issue Date:: 2017-08-28

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Field	Value	Language
dc.contributor.author	Xin, JN	en_US
dc.contributor.author	Du, X	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2017-08-28	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2017, pp. 613 - 618	en_US
dc.identifier.isbn	9781509060672	en_US
dc.identifier.issn	1945-7871	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120410
dc.description.abstract	© 2017 IEEE. Robust visual tracking for outdoor vehicle is still a challenging problem due to large appearance variations caused by illumination variation, occlusion and scale variation, etc. In this paper, a deep-learning-based approach for robust outdoor vehicle tracking is proposed. Firstly, a stacked denoising auto-encoder is pre-trained to learn the feature representation way of images. Then, a k-sparse constraint is added to the stacked denoising auto-encoder and the encoder of k-sparse stacked denoising auto-encoder (kSSDAE) is connected with a classification layer to construct a classification neural network. After fine-tuning, the classification neural network is applied to online tracking under particle filter framework. Extensive tracking experiments are conducted on a challenging single object online tracking evaluation platform benchmark to verify the effectiveness of our tracker. Experiments show that our tracker outperforms most state-of-the-art trackers.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo	en_US
dc.relation.isbasedon	10.1109/ICME.2017.8019329	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep learning for robust outdoor vehicle visual tracking	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	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.declined	2017-11-21T13:04:47.123+1100
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Robust visual tracking for outdoor vehicle is still a challenging problem due to large appearance variations caused by illumination variation, occlusion and scale variation, etc. In this paper, a deep-learning-based approach for robust outdoor vehicle tracking is proposed. Firstly, a stacked denoising auto-encoder is pre-trained to learn the feature representation way of images. Then, a k-sparse constraint is added to the stacked denoising auto-encoder and the encoder of k-sparse stacked denoising auto-encoder (kSSDAE) is connected with a classification layer to construct a classification neural network. After fine-tuning, the classification neural network is applied to online tracking under particle filter framework. Extensive tracking experiments are conducted on a challenging single object online tracking evaluation platform benchmark to verify the effectiveness of our tracker. Experiments show that our tracker outperforms most state-of-the-art trackers.

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http://hdl.handle.net/10453/120410