A modified Mahalanobis distance for human detection in out-door environments

Chen, Y; Wu, Q; He, X; Jia, W; Hintz, T

A modified Mahalanobis distance for human detection in out-door environments

Chen, Y Wu, Q

He, X

Jia, W

Hintz, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 the 1st IEEE International Conference on Ubi-Media Computing and Workshops, U-Media2008, 2008, pp. 243 - 248
Issue Date:: 2008-09-26

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Field	Value	Language
dc.contributor.author	Chen, Y	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Hintz, T	en_US
dc.date.issued	2008-09-26	en_US
dc.identifier.citation	Proceedings - 2008 the 1st IEEE International Conference on Ubi-Media Computing and Workshops, U-Media2008, 2008, pp. 243 - 248	en_US
dc.identifier.isbn	9781424418657	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10856
dc.description.abstract	This paper proposes a novel method for human detection from static images based on pixel structure of input images. Each image is divided into four parts, and a weight is assigned to each part of the image. In training stage, all sample images including human images and non-human images are used to construct a Mahalanobis distance map through statistically analyzing the difference between the different blocks on each original image. A projection matrix will be created with Linear Discriminant Method (LDM) based on the Mahalanobis distance map. This projection matrix will be used to transform multidimensional feature vectors into one dimensional feature domain according to a pre-calculated threshold to distinguish human figures from non-human figures. In comparison with the method without introducing weights, the proposed method performs much better. Encouraging experimental results have been obtained based on MIT dataset and our own dataset. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2008 the 1st IEEE International Conference on Ubi-Media Computing and Workshops, U-Media2008	en_US
dc.relation.isbasedon	10.1109/UMEDIA.2008.4570897	en_US
dc.title	A modified Mahalanobis distance for human detection in out-door environments	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Lanzhou, China	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/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
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	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper proposes a novel method for human detection from static images based on pixel structure of input images. Each image is divided into four parts, and a weight is assigned to each part of the image. In training stage, all sample images including human images and non-human images are used to construct a Mahalanobis distance map through statistically analyzing the difference between the different blocks on each original image. A projection matrix will be created with Linear Discriminant Method (LDM) based on the Mahalanobis distance map. This projection matrix will be used to transform multidimensional feature vectors into one dimensional feature domain according to a pre-calculated threshold to distinguish human figures from non-human figures. In comparison with the method without introducing weights, the proposed method performs much better. Encouraging experimental results have been obtained based on MIT dataset and our own dataset. © 2008 IEEE.

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http://hdl.handle.net/10453/10856