The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 로컬 기능 구성을 기반으로 하는 차량 분류 시스템에 대해 설명합니다. 우리는 이미 우리 시스템이 실외 환경에서 차량 인식에 매우 잘 작동한다는 것을 입증했습니다. 알고리즘은 고유 창 방법을 일반화한 이전 작업을 기반으로 합니다. 이 방법에는 다음과 같은 세 가지 장점이 있습니다. (1) 차량의 일부가 가려져도 감지할 수 있습니다. (2) 차선 이탈로 인해 차량이 평행이동하는 경우에도 감지할 수 있습니다. (3) 입력 영상에서 차량 영역의 분할이 필요하지 않습니다. 그러나 이 방법에는 문제가 있습니다. 뷰 기반이기 때문에 우리 시스템에는 대상 차량의 모델 이미지가 필요합니다. 대상 차량의 실제 이미지를 수집하는 것은 일반적으로 시간이 많이 걸리고 어려운 작업입니다. 모든 대상 차량의 이미지 수집 작업을 쉽게 하기 위해 우리 시스템을 컴퓨터 그래픽(CG) 모델에 적용하여 실제 이미지에서 차량을 인식합니다. 실외 실험을 통해 차량의 실제 이미지를 수집하는 것보다 CG 모델을 사용하는 것이 시스템에 효과적이라는 것을 확인했습니다. 실험 결과 CG 모델이 실제 이미지에서 차량을 인식할 수 있으며, 우리 시스템이 차량을 분류할 수 있음을 확인했습니다.
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부
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
부
@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},}
부
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 -