Adaptive stick-like features for human detection based on multi-scale feature fusion scheme

Wang, S; Du, R; Wu, Q; He, XJ

Adaptive stick-like features for human detection based on multi-scale feature fusion scheme

Wang, S Du, R Wu, Q

He, XJ

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010, 2010, pp. 375 - 380
Issue Date:: 2010-12-01

Closed Access

	Filename	Description	Size
	2010000746OK.pdf		2.03 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Du, R	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, XJ https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010, 2010, pp. 375 - 380	en_US
dc.identifier.isbn	9780769542713	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16252
dc.description.abstract	Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale images. Various object features are exposed on multiple levels. To further boost the overall performance, a fusion scheme is established using scores obtained at various levels which integrates decision results with different scales to make the final decision. Unlike other score-based fusion methods, this paper re-formulates the fusion process through a supervised learning. Therefore, our fusion approach can better distinguish subtle difference between human objects and non-human objects. Furthermore, in our approach, we are able to use simpler weak features for boosting and hence alleviate the training complexity existed in most of AdaBoost training approaches. Encouraging results are obtained on a well recognized benchmark database. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010	en_US
dc.relation.isbasedon	10.1109/DICTA.2010.70	en_US
dc.title	Adaptive stick-like features for human detection based on multi-scale feature fusion scheme	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
dc.location.activity	Sydney, 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/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale images. Various object features are exposed on multiple levels. To further boost the overall performance, a fusion scheme is established using scores obtained at various levels which integrates decision results with different scales to make the final decision. Unlike other score-based fusion methods, this paper re-formulates the fusion process through a supervised learning. Therefore, our fusion approach can better distinguish subtle difference between human objects and non-human objects. Furthermore, in our approach, we are able to use simpler weak features for boosting and hence alleviate the training complexity existed in most of AdaBoost training approaches. Encouraging results are obtained on a well recognized benchmark database. © 2010 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16252