Pixel Structure Based on Hausdorff Distance for Human Detection in Outdoor Environments

Chen, Y; Wu, Q; He, S; Jia, W; Hintz, TB

Pixel Structure Based on Hausdorff Distance for Human Detection in Outdoor Environments

Chen, Y Wu, Q

He, S

Jia, W

Hintz, TB

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Publisher:: IEEE Computer Society
Publication Type:: Conference Proceeding
Citation:: Digital Image Computing Techniques and Applications, 2007, pp. 67 - 72
Issue Date:: 2007-01

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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, S 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, TB	en_US
dc.contributor.editor	Bottema, MEA	en_US
dc.date	2007-12-03	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	Digital Image Computing Techniques and Applications, 2007, pp. 67 - 72	en_US
dc.identifier.isbn	0-7695-3067-2	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2421
dc.description.abstract	This paper proposes a novel method for human detection from static images based on pixel structure of input images. In training stage, all sample images consisting of human images and non-human images are used to construct a Hausdorff distance map based on 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 Hausdorff distance map. This projection matrix will be used to transform multidimensional feature vectors (distance maps of testing images) into a feature in a one-dimensional domain. The decision will be made on the simple one dimensional feature domain according to a precalculated threshold to distinguish human figures from non-human figures. In comparison with the common method based on Mahalanobis distance maps, the proposed method based on Hausdorff distance maps performs much better. Encouraging experimental results have been obtained using 800 human images and 800 non-human images.	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation.ispartof	Digital Image Computing Techniques and Applications	en_US
dc.relation.isbasedon	10.1109/DICTA.2007.17	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	Pixel Structure Based on Hausdorff Distance for Human Detection in Outdoor Environments	en_US
dc.type	Conference Proceeding
utslib.location	Los Alamitos, USA	en_US
utslib.location.activity	Glenelg, Australia	en_US
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	Glenelg, 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/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	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Los Alamitos, USA	en_US
pubs.start-date	2007-12-03	en_US

Abstract:

This paper proposes a novel method for human detection from static images based on pixel structure of input images. In training stage, all sample images consisting of human images and non-human images are used to construct a Hausdorff distance map based on 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 Hausdorff distance map. This projection matrix will be used to transform multidimensional feature vectors (distance maps of testing images) into a feature in a one-dimensional domain. The decision will be made on the simple one dimensional feature domain according to a precalculated threshold to distinguish human figures from non-human figures. In comparison with the common method based on Mahalanobis distance maps, the proposed method based on Hausdorff distance maps performs much better. Encouraging experimental results have been obtained using 800 human images and 800 non-human images.

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

http://hdl.handle.net/10453/2421