DocumentCode
3021004
Title
Application of the Reeb Graph Technique to Vehicle Occupant´s Head Detection in Low-resolution Range Images
Author
Devarakota, Pandu Rangarao ; Castillo-Franco, Marta ; Ginhoux, Romuald ; Mirbach, Bruno ; Ottersten, Björn
Author_Institution
IEE S.A., Contern
fYear
2007
fDate
17-22 June 2007
Firstpage
1
Lastpage
8
Abstract
In [3], a low-resolution range sensor was investigated for an occupant classification system that distinguish person from child seats or an empty seat. The optimal deployment of vehicle airbags for maximum protection moreover requires information about the occupant´s size and position. The detection of occupant´s position involves the detection and localization of occupant´s head. This is a challenging problem as the approaches based on local shape analysis (in 2D or 3D) alone are not robust enough as other parts of the person´s body like shoulders, knee may have similar shapes as the head. This paper discusses and investigate the potential of a Reeb graph approach to describe the topology of vehicle occupants in terms of a skeleton. The essence of the proposed approach is that an occupant sitting in a vehicle has a typical topology which leads to different branches of a Reeb Graph and the possible location of the occupant´s head are thus the end points of the Reeb graph. The proposed method is applied on real 3D range images and is compared to Ground truth information. Results show the feasibility of using topological information to identify the position of occupant´s head.
Keywords
graph theory; image classification; image sensors; road safety; traffic engineering computing; 3D range images; Reeb graph technique; ground truth information; local shape analysis; low-resolution range images; low-resolution range sensor; occupant classification system; vehicle airbags; vehicle occupant head detection; Head; Knee; Protection; Robustness; Sensor systems; Shape; Skeleton; Topology; Vehicle detection; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference on
Conference_Location
Minneapolis, MN
ISSN
1063-6919
Print_ISBN
1-4244-1179-3
Electronic_ISBN
1063-6919
Type
conf
DOI
10.1109/CVPR.2007.383450
Filename
4270448
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