A new edge feature for head-shoulder detection

Wang, S; Zhang, J; Miao, Z

A new edge feature for head-shoulder detection

Wang, S Zhang, J

Miao, Z

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 2822 - 2826
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Miao, Z	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 2822 - 2826	en_US
dc.identifier.isbn	9781479923410	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28025
dc.description.abstract	In this work, we introduce a new edge feature to improve the head-shoulder detection performance. Since Head-shoulder detection is much vulnerable to vague contour, our new edge feature is designed to extract and enhance the head-shoulder contour and suppress the other contours. The basic idea is that head-shoulder contour can be predicted by filtering edge image with edge patterns, which are generated from edge fragments through a learning process. This edge feature can significantly enhance the object contour such as human head and shoulder known as En-Contour. To evaluate the performance of the new En-Contour, we combine it with HOG+LBP [1] as HOG+LBP+En-Contour. The HOG+LBP is the state-of-the-art feature in pedestrian detection. Because the human head-shoulder detection is a special case of pedestrian detection, we also use it as our baseline. Our experiments have indicated that this new feature significantly improve the HOG+LBP. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICIP.2013.6738581	en_US
dc.title	A new edge feature for head-shoulder detection	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 - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this work, we introduce a new edge feature to improve the head-shoulder detection performance. Since Head-shoulder detection is much vulnerable to vague contour, our new edge feature is designed to extract and enhance the head-shoulder contour and suppress the other contours. The basic idea is that head-shoulder contour can be predicted by filtering edge image with edge patterns, which are generated from edge fragments through a learning process. This edge feature can significantly enhance the object contour such as human head and shoulder known as En-Contour. To evaluate the performance of the new En-Contour, we combine it with HOG+LBP [1] as HOG+LBP+En-Contour. The HOG+LBP is the state-of-the-art feature in pedestrian detection. Because the human head-shoulder detection is a special case of pedestrian detection, we also use it as our baseline. Our experiments have indicated that this new feature significantly improve the HOG+LBP. © 2013 IEEE.

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http://hdl.handle.net/10453/28025