Simultaneously detect and segment pedestrian

Wang, S; Miao, Z; Zhang, J

Simultaneously detect and segment pedestrian

Wang, S Miao, Z Zhang, J

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Publication Type:: Conference Proceeding
Citation:: Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013, 2013
Issue Date:: 2013-11-29

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Miao, Z	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2013-11-29	en_US
dc.identifier.citation	Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013, 2013	en_US
dc.identifier.isbn	9781479916047	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28028
dc.description.abstract	We present a framework to simultaneously detect and segment pedestrian in images. Our work is based on part-based method. We first segment the image into superpixels, then assemble superpixels into body part candidates by comparing the assembled shape with pre-built template library. A 'structure-based' shape matching algorithm is developed to measure the shape similarity. All the body part candidates are input into our modified AND/OR graph to generate the most reasonable combination. The graph describes the possible variation of body configuration and model the constrain relationship between body parts. We perform comparison experiments on the public database and the results show the effectiveness of our framework. © 2013 IEEE.	en_US
dc.relation.ispartof	Electronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013	en_US
dc.relation.isbasedon	10.1109/ICMEW.2013.6618294	en_US
dc.title	Simultaneously detect and segment pedestrian	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
dc.location.activity	San Jose, USA	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:

We present a framework to simultaneously detect and segment pedestrian in images. Our work is based on part-based method. We first segment the image into superpixels, then assemble superpixels into body part candidates by comparing the assembled shape with pre-built template library. A 'structure-based' shape matching algorithm is developed to measure the shape similarity. All the body part candidates are input into our modified AND/OR graph to generate the most reasonable combination. The graph describes the possible variation of body configuration and model the constrain relationship between body parts. We perform comparison experiments on the public database and the results show the effectiveness of our framework. © 2013 IEEE.

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http://hdl.handle.net/10453/28028