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Classification of vehicle occupants using 3D image sequences
IEE S.A., Zone Industrielle, 2b, Route de Tr`eves L-2632 Findel, Luxembourg.
IEE S.A., Zone Industrielle, 2b, Route de Tr`eves L-2632 Findel, Luxembourg.
IEE S.A., Zone Industrielle, 2b, Route de Tr`eves L-2632 Findel, Luxembourg.
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0003-2298-6774
2005 (English)In: 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, IEEE , 2005, 717-720 p.Conference paper, Published paper (Refereed)
Abstract [en]

The deployment of vehicle airbags for maximum protection requires information about the occupant's position, movement, weight, size etc. Specifically it is desirable to discriminate between adults, children, front- or rear faced child seats, objects put on the seat or simply empty seats. 2D images lack depth information about the object and are very sensitive to illumination conditions. Herein, occupant position classification techniques are developed based on low resolution 3D image sequences. The proposed methods are of low complexity and high reliability allowing real time implementation and meeting the rigorous requirements for passenger safety systems. Features are extracted from the 3D image sequences and a Sequential Forward Search (SFS) feature subset selection algorithm is employed to reduce the size of the feature set. Two classification techniques are evaluated, the B ayes quadratic classifier and the polynomial classifier. We present the classification results based on a large set of measurements from the low resolution 3D image sequences. The full scale tests have been conducted on a wide rance of realistic situations (adults/children/child seats etc.) which may occur in a vehicle.

Place, publisher, year, edition, pages
IEEE , 2005. 717-720 p.
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keyword [en]
Data mining, Feature extraction, Image resolution, Image sequences, Lighting, Polynomials, Protection, Real time systems, Safety, Vehicles
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-34977DOI: 10.1109/ICASSP.2005.1416109ISI: 000229404203179Scopus ID: 2-s2.0-33646801981OAI: oai:DiVA.org:kth-34977DiVA: diva2:426966
Conference
30th IEEE International Conference on Acoustics, Speech, and Signal Processing Philadelphia, PA, MAR 19-23, 2005
Note
QC 20110627Available from: 2011-06-27 Created: 2011-06-17 Last updated: 2012-01-23Bibliographically approved

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Ottersten, Björn

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  • apa
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