Vehicle Classification System with Local-Feature Based Algorithm Using CG Model Images

Tatsuya YOSHIDA; Shirmila MOHOTTALA; Masataka KAGESAWA; Katsushi IKEUCHI

Vehicle Classification System with Local-Feature Based Algorithm Using CG Model Images

Tatsuya YOSHIDA, Shirmila MOHOTTALA, Masataka KAGESAWA, Katsushi IKEUCHI

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This paper describes our vehicle classification system, which is based on local-feature configuration. We have already demonstrated that our system works very well for vehicle recognition in outdoor environments. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) It can detect even if parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to veering out of the lanes. (3) It does not require segmentation of vehicle areas from input images. However, this method does have a problem. Because it is view-based, our system requires model images of the target vehicles. Collecting real images of the target vehicles is generally a time consuming and difficult task. To ease the task of collecting images of all target vehicles, we apply our system to computer graphics (CG) models to recognize vehicles in real images. Through outdoor experiments, we have confirmed that using CG models is effective than collecting real images of vehicles for our system. Experimental results show that CG models can recognize vehicles in real images, and confirm that our system can classify vehicles.

Publication: IEICE TRANSACTIONS on Information Vol.E85-D No.11 pp.1745-1752

Publication Date: 2002/11/01

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Type of Manuscript: Special Section PAPER (Special Issue on Information System Technologies for ITS)

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Tatsuya YOSHIDA, Shirmila MOHOTTALA, Masataka KAGESAWA, Katsushi IKEUCHI, "Vehicle Classification System with Local-Feature Based Algorithm Using CG Model Images" in IEICE TRANSACTIONS on Information, vol. E85-D, no. 11, pp. 1745-1752, November 2002, doi: .
Abstract: This paper describes our vehicle classification system, which is based on local-feature configuration. We have already demonstrated that our system works very well for vehicle recognition in outdoor environments. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) It can detect even if parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to veering out of the lanes. (3) It does not require segmentation of vehicle areas from input images. However, this method does have a problem. Because it is view-based, our system requires model images of the target vehicles. Collecting real images of the target vehicles is generally a time consuming and difficult task. To ease the task of collecting images of all target vehicles, we apply our system to computer graphics (CG) models to recognize vehicles in real images. Through outdoor experiments, we have confirmed that using CG models is effective than collecting real images of vehicles for our system. Experimental results show that CG models can recognize vehicles in real images, and confirm that our system can classify vehicles.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_11_1745/_p

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@ARTICLE{e85-d_11_1745,
author={Tatsuya YOSHIDA, Shirmila MOHOTTALA, Masataka KAGESAWA, Katsushi IKEUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Vehicle Classification System with Local-Feature Based Algorithm Using CG Model Images},
year={2002},
volume={E85-D},
number={11},
pages={1745-1752},
abstract={This paper describes our vehicle classification system, which is based on local-feature configuration. We have already demonstrated that our system works very well for vehicle recognition in outdoor environments. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) It can detect even if parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to veering out of the lanes. (3) It does not require segmentation of vehicle areas from input images. However, this method does have a problem. Because it is view-based, our system requires model images of the target vehicles. Collecting real images of the target vehicles is generally a time consuming and difficult task. To ease the task of collecting images of all target vehicles, we apply our system to computer graphics (CG) models to recognize vehicles in real images. Through outdoor experiments, we have confirmed that using CG models is effective than collecting real images of vehicles for our system. Experimental results show that CG models can recognize vehicles in real images, and confirm that our system can classify vehicles.},
keywords={},
doi={},
ISSN={},
month={November},}

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TY - JOUR
TI - Vehicle Classification System with Local-Feature Based Algorithm Using CG Model Images
T2 - IEICE TRANSACTIONS on Information
SP - 1745
EP - 1752
AU - Tatsuya YOSHIDA
AU - Shirmila MOHOTTALA
AU - Masataka KAGESAWA
AU - Katsushi IKEUCHI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2002
AB - This paper describes our vehicle classification system, which is based on local-feature configuration. We have already demonstrated that our system works very well for vehicle recognition in outdoor environments. The algorithm is based on our previous work, which is a generalization of the eigen-window method. This method has the following three advantages: (1) It can detect even if parts of the vehicles are occluded. (2) It can detect even if vehicles are translated due to veering out of the lanes. (3) It does not require segmentation of vehicle areas from input images. However, this method does have a problem. Because it is view-based, our system requires model images of the target vehicles. Collecting real images of the target vehicles is generally a time consuming and difficult task. To ease the task of collecting images of all target vehicles, we apply our system to computer graphics (CG) models to recognize vehicles in real images. Through outdoor experiments, we have confirmed that using CG models is effective than collecting real images of vehicles for our system. Experimental results show that CG models can recognize vehicles in real images, and confirm that our system can classify vehicles.
ER -