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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.6
- 수록면
- 432 - 438 (7page)
- DOI
- 10.5302/J.ICROS.2017.17.0017
이용수
초록· 키워드
오류제보하기
Developing algorithms for real-time traffic sign recognition is usually accompanied by constrained computing time and limited resources, such as memory and processing power. This paper addressed the improvement of traffic sign classification by efficiently reducing the dimensionality of the feature space. The proposed approach reduced the demands on computational resources while training and running the application on an embedded system. Dimensionality reduction can be subdivided into feature space projection and feature selection. Feature space projection requires additional computational costs at runtime. For this reason, the usability of feature subset selection was investigated and evaluated on a large traffic sign dataset. In addition, a novel approach to feature selection for traffic sign recognition based on the Bayes factor criterion was proposed. In contrast to existing feature selection methods relying on extensive parameter testing, the proposed approach computed the dimensionality of the final feature subset automatically, requiring only a small number of iterations for the interpretable input parameter. Extensive evaluations on a large dataset showed that the computational costs at runtime could be reduced by over 70% with efficient feature selection.
목차
Abstract
I. 서론
Ⅱ. 기존의 특징점 선택 알고리즘 분석
III. 제안하는 특징점 선택 알고리즘
Ⅳ. 실험결과
V. 결론
REFERENCES
참고문헌 (22)
참고문헌 신청
[학술지(정기간행물)] - A. Mogelmose / 2012 / Visionbased traffic sign detection and analysis for intelligent driver assistance systems : Perspectives and survey / IEEE Transactions on Intelligent Transportation Systems 13 (4) : 1484 ~ 1497
[학술지(정기간행물)] - H. Liu / 2013 / Intelligent video systems and analytics : A survey / IEEE Transactions on Industrial Informatics 9 (3) : 1222 ~ 1233
[학술지(정기간행물)] - J. Stallkamp / 2012 / Man vs. computer : Benchmarking machine learning algorithms for traffic sign recognition / IEEE Transactions on Neural networks 32 : 323 ~ 332
[학술지(정기간행물)] - G. Piccioli / 1996 / Robust method for road sign detection and recognition / Image and Vision Computing 14 (3) : 209 ~ 223
[학술지(정기간행물)] - X. W. Gao / 2006 / Recognition of traffic signs based on their colour and shape features extracted using human vision models / Journal of Visual Communication and Image Representation 17 (4) : 675 ~ 685
[학술지(정기간행물)] - H. Liu / 2013 / Intelligent video systems and analytics : A survey / IEEE Transactions on Industrial Informatics 9 (3) : 1222 ~ 1233
[학술지(정기간행물)] - J. Stallkamp / 2012 / Man vs. computer : Benchmarking machine learning algorithms for traffic sign recognition / IEEE Transactions on Neural networks 32 : 323 ~ 332
[학술지(정기간행물)] - G. Piccioli / 1996 / Robust method for road sign detection and recognition / Image and Vision Computing 14 (3) : 209 ~ 223
[학술지(정기간행물)] - X. W. Gao / 2006 / Recognition of traffic signs based on their colour and shape features extracted using human vision models / Journal of Visual Communication and Image Representation 17 (4) : 675 ~ 685
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UCI(KEPA) : I410-ECN-0101-2018-003-000895365