Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 876451, 9 pages
http://dx.doi.org/10.1155/2014/876451
Research Article

A Multistep Framework for Vision Based Vehicle Detection

1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China

Received 1 May 2014; Revised 17 July 2014; Accepted 11 August 2014; Published 27 August 2014

Academic Editor: Yantao Shen

Copyright © 2014 Hai Wang and Yingfeng Cai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Z. Sun, G. Bebis, and R. Miller, “On-road vehicle detection using evolutionary Gabor filter optimization,” IEEE Transactions on Intelligent Transportation System, vol. 6, no. 2, pp. 125–137, 2005. View at Publisher · View at Google Scholar
  2. G. Kim and J. Cho, “Vision-based vehicle detection and inter-vehicle distance estimation,” in Proceedings of the 12th International Conference on Control, Automation and Systems (ICCAS '12), pp. 625–629, October 2012. View at Scopus
  3. P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph-based image segmentation,” International Journal of Computer Vision, vol. 59, no. 2, pp. 167–181, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. X. He, R. Zemel, and D. Ray, “Learning and incorporating top-down cues in image segmentation,” in Proceedings of the 9th European Conference on Computer Vision, pp. 338–351, 2006.
  5. J. Tighe and S. Lazebnik, “Superparsing: scalable nonparametric image parsing with superpixels,” in Proceedings of the 11th European Conference on Computer Vision: Part V (ECCV '10), pp. 352–365, 2010.
  6. B. Fulkerson, A. Vedaldi, and S. Soatto, “Class segmentation and object localization with superpixel neighborhoods,” in Proceedings of the International Conference on Computer Vision, pp. 670–677, 2009.
  7. D. Hoiem, A. A. Efros, and M. Hebert, “Recovering surface layout from an image,” International Journal of Computer Vision, vol. 75, no. 1, pp. 151–172, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Saxena, M. Sun, and A. Y. Ng, “Make3D: learning 3D scene structure from a single still image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 824–840, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Liu, S. Gould, and D. Koller, “Single image depth estimation from predicted semantic labels,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 1253–1260, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Chang and C. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Liu, X. Wen, B. Duan, H. Yuan, and N. Wang, “Rear vehicle detection and tracking for lane change assist,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV '07), pp. 252–257, June 2007. View at Scopus
  12. Z. Sun, G. Bebis, and R. Miller, “Monocular precrash vehicle detection: features and classifiers,” IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 2019–2034, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Sivaraman and M. M. Trivedi, “Active learning for on-road vehicle detection: a comparative study,” Machine Vision and Applications, vol. 25, no. 3, pp. 599–611, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. S. S. Teoh and T. Bräunl, “Symmetry-based monocular vehicle detection system,” Machine Vision and Applications, vol. 23, no. 5, pp. 831–842, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Sindoori, K. S. Ravichandran, and B. Santhi, “Adaboost technique for vehicle detection in aerial surveillance,” International Journal of Engineering and Technology, vol. 5, no. 2, pp. 765–769, 2013. View at Google Scholar · View at Scopus
  16. J. Cui, F. Liu, Z. Li, and Z. Jia, “Vehicle localisation using a single camera,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV'10), pp. 871–876, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. T. T. Son and S. Mita, “Car detection using multi-feature selection for varying poses,” in Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 507–512, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Acunzo, Y. Zhu, B. Xie, and G. Baratoff, “Context-adaptive approach for vehicle detection under varying lighting conditions,” in Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems (ITSC '07), pp. 654–660, Seattle, Wash, USA, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. C. T. Lin, S. C. Hsu, J. F. Lee et al., “Boosted vehicle detection using local and global features,” Journal of Signal & Information Processing, vol. 4, no. 3, 2013. View at Google Scholar
  20. O. Ludwig Jr. and U. Nunes, “Improving the generalization properties of neural networks: an application to vehicle detection,” in Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (ITSC '08), pp. 310–315, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. V. Nair and G. E. Hinton, “3D object recognition with deep belief nets,” in Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS '09), pp. 1339–1347, December 2009. View at Scopus
  23. F. Wood and G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Tech. Rep., Brown University, 2012. View at Google Scholar
  24. P. Bergmiller, M. Botsch, J. Speth, and U. Hofmann, “Vehicle rear detection in images with generalized radial-basis-function classifiers,” in Proceeding of the 2008 IEEE Intelligent Vehicles Symposium (IV '08), pp. 226–233, Eindhoven, The Netherlands, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. D. Alonso, L. Salgado, and M. Nieto, “Robust vehicle detection through multidimensional classification for on board video based systems,” in Proceedings of the 14th IEEE International Conference on Image Processing (ICIP '07), pp. IV321–IV324, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Southall, M. Bansal, and J. Eledath, “Real-time vehicle detection for highway driving,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 541–548, June 2009. View at Publisher · View at Google Scholar · View at Scopus