Robust vehicle Detection in Rainy Situation with Adaboost Using CLAHE 


Vol. 41,  No. 12, pp. 1978-1984, Dec.  2016


PDF
  Abstract

This paper proposes a robust vehicle detecting method by using Adaboost and CLAHE(Contrast-Limit Adaptive Histogram Equalization). We propose two method to detect vehicle effectively. First, we are able to judge rainy and night by converting RGB value to brightness. Second, we can detect a taillight, designate a ROI(Region Of Interest) by using CLAHE. And then, we choose an Adaboost algorithm by comparing traditional vehicle detecting method such as GMM(Gaussian Mixture Model), Optical flow and Adaboost. In this paper, we use proposed method and get better performance of detecting vehicle. The precision and recall score of proposed method are 0.85 and 0.87. That scores are better than GMM and optical flow.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

S. Kang and D. S. Han, "Robust vehicle Detection in Rainy Situation with Adaboost Using CLAHE," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 12, pp. 1978-1984, 2016. DOI: .

[ACM Style]

Seokjun Kang and Dong Seog Han. 2016. Robust vehicle Detection in Rainy Situation with Adaboost Using CLAHE. The Journal of Korean Institute of Communications and Information Sciences, 41, 12, (2016), 1978-1984. DOI: .

[KICS Style]

Seokjun Kang and Dong Seog Han, "Robust vehicle Detection in Rainy Situation with Adaboost Using CLAHE," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 12, pp. 1978-1984, 12. 2016.

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880