Pedestrian detection using a mixture mask model

Liu, X; Song, M; Zhang, L; Tao, D; Bu, J; Chen, C

Pedestrian detection using a mixture mask model

Liu, X Song, M Zhang, L Tao, D

Bu, J Chen, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control, ICNSC 2012, 2012, pp. 271 - 276
Issue Date:: 2012-07-04

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Field	Value	Language
dc.contributor.author	Liu, X	en_US
dc.contributor.author	Song, M	en_US
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Bu, J	en_US
dc.contributor.author	Chen, C	en_US
dc.date.issued	2012-07-04	en_US
dc.identifier.citation	Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control, ICNSC 2012, 2012, pp. 271 - 276	en_US
dc.identifier.isbn	9781467303880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22956
dc.description.abstract	Pedestrian detection is one of the fundamental tasks of an intelligent transportation system. Differences in illumination, posture and point of view make pedestrian detection confront with great challenges. In this paper, we focus on the main defect in the existing methods: the interference of the non-person area. Firstly, we use mapping vectors to map the original feature matrix to the different mask spaces, then using a part-based structure, we implicitly formulate the model into a multiple-instance problem, and finally use a MIL-SVM to solve the problem. Based on the model, we design a system which can find pedestrians from pictures. We give detailed description on the model and the system in this paper. The experimental results on public data sets show that our method decreases the miss rate greatly. © 2012 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120103730
dc.relation.ispartof	Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control, ICNSC 2012	en_US
dc.relation.isbasedon	10.1109/ICNSC.2012.6204929	en_US
dc.title	Pedestrian detection using a mixture mask model	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Beijing, 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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Pedestrian detection is one of the fundamental tasks of an intelligent transportation system. Differences in illumination, posture and point of view make pedestrian detection confront with great challenges. In this paper, we focus on the main defect in the existing methods: the interference of the non-person area. Firstly, we use mapping vectors to map the original feature matrix to the different mask spaces, then using a part-based structure, we implicitly formulate the model into a multiple-instance problem, and finally use a MIL-SVM to solve the problem. Based on the model, we design a system which can find pedestrians from pictures. We give detailed description on the model and the system in this paper. The experimental results on public data sets show that our method decreases the miss rate greatly. © 2012 IEEE.

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http://hdl.handle.net/10453/22956