Advanced SearchSearch Tips
Vision-based Vehicle Detection Using HOG and OS Fuzzy-ELM
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Vision-based Vehicle Detection Using HOG and OS Fuzzy-ELM
Yoon, Changyong; Lee, Heejin;
  PDF(new window)
This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.
ELM;Vehicle Detection;SLFN;HOG;Computer Vision;
 Cited by
J. H. Yu, Y. J. Han, and S. H. Han, "Fast Vehicle Detection based on Haarlike and Vehicle Tracking using SURF Method," Korean Society of Computer Information, vol. 17, no. 1, pp. 71-80, 2012.

M. S. Choi, H. J. Lee, M. T. Noh, and J. C. Sim "Vehicle Detection Scheme Based on a Boosting Classifier with Histogram of Oriented Gradient (HOG) Features and Image Segmentation," Journal of KIISE, vol. 16, no. 10, pp. 955-961, Oct. 2010.

Y. H. Lee, J. Y. Ko, J. H. Suk, T. M. Roh, and J. C. Shim, "Pedestrian Recognition using Adaboost Algorithm based on Cascade Method by Curvature and HOG," Journal of KIISE : Computing Practices and Letters, vol. 16, no. 6, pp. 654-662, Jun. 2010.

G. B. Huang, N. Y. Liang, H. J. Rong, P. Saratchandran, and N. Sundararajan, "On-Line Sequential Extreme Learning Machine," The IASTED International Conference on CI, Calgary, Canada, July 4-6, 2005.

Z. Saad, M. K. Osman, Z. I. Zulkafli, S. Ishak, "Vehicle Recognition System Using Singular Value Decomposition (SVD) and Levenberg-Marquardt," Computational Intelligence, Modelling and Simulation, 2009. CSSim '09. International Conference on, pp. 187-191, 7-9 Sept. 2009.

N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan, "A Fast and Accurate Onine Sequential Learning Algorithm for Feedforward Networks," IEEE Trans. on Neural Networks, vol. 17, no. 6, pp. 1411-1423, Nov. 2006. crossref(new window)

N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.

J.-S. R. Jang and C.-T. Sun, "Functional equivalence between radial basis function networks and fuzzy inference systems," IEEE Trans. Neural Netw., vol. 4, no. 1, pp. 156-159, Jan. 1993. crossref(new window)

H. J. Rong, G. B. Huang, N. Sundararajan and P. Saratchandran, "Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems," IEEE Trans. Systems, Man, And Cybernetics-Part B: Cybernetics, vol. 39, no. 4, pp. 1067-1072, Aug. 2009. crossref(new window)

H. M. Eum, S. Y. Jang, H. J. Lee, M. Y. Park and C. Y. Yoon, "Human Detection and Fuzzy Temperature Control System for Energy Reduction of Coolling Device in Elevator," Journal of Korean Institute of Intelligent Systems, vol. 25, no. 2, pp. 147-154, April. 2015. crossref(new window)