Pedestrian detection using hybrid statistical feature

Wu, Q; Du, C; Yang, J; He, X; Chen, Y

Pedestrian detection using hybrid statistical feature

Wu, Q

Du, C Yang, J He, X

Chen, Y

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 2008, pp. 101 - 106
Issue Date:: 2008-12-31

Closed Access

	Filename	Description	Size
	2008000784OK.pdf		893.06 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Du, C	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Chen, Y	en_US
dc.date.issued	2008-12-31	en_US
dc.identifier.citation	Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 2008, pp. 101 - 106	en_US
dc.identifier.isbn	9781424422951	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10832
dc.description.abstract	A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of Gait Energy Image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA Gait Database and the additional nonhuman objects data. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008	en_US
dc.relation.isbasedon	10.1109/MMSP.2008.4665056	en_US
dc.title	Pedestrian detection using hybrid statistical feature	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Cairns, Queensland, 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.publication-status	Published	en_US

Abstract:

A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of Gait Energy Image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA Gait Database and the additional nonhuman objects data. © 2008 IEEE.

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

http://hdl.handle.net/10453/10832