Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 701058, 11 pages
http://dx.doi.org/10.1155/2014/701058
Research Article

Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

1College of Computer Science and Technology, Jilin University, Changchun 130012, China
2State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
3Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

Received 13 May 2014; Accepted 25 June 2014; Published 24 July 2014

Academic Editor: Weichao Sun

Copyright © 2014 Wenhui Li et al. 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: a review,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 694–711, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. M. Chan, S. S. Huang, L. C. Fu, P. Y. Hsiao, and M. F. Lo, “Vehicle detection and tracking under various lighting conditions using a particle filter,” IET Intelligent Transport Systems, vol. 6, no. 1, pp. 1–8, 2012. View at Publisher · View at Google Scholar
  3. B. Lin, Y. Lin, L. Fu et al., “Integrating appearance and edge features for sedan vehicle detection in the blind-spot area,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 737–747, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Hwang, K. Huh, and D. Lee, “Vision-based vehicle detection and tracking algorithm design,” Optical Engineering, vol. 48, no. 12, Article ID 127201, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Southall, M. Bansal, and J. Eledath, “Real-time vehicle detection for highway driving,” in Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 541–548, Miami, Fla, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Y. Chen, G. R. Chen, and Y. W. Wang, “Real-time dynamic vehicle detection on resource-limited mobile platform,” IET Computer Vision, vol. 7, no. 2, pp. 81–89, 2013. View at Google Scholar
  7. Y. Tsai, K. Huang, C. Tsai, and L. Chen, “An exploration of on-road vehicle detection using hierarchical scaling schemes,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 3937–3940, Hong Kong, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Chang and C. Cho, “Online boosting for vehicle detection,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, no. 3, pp. 892–902, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Sivaraman and M. M. Trivedi, “A general active-learning framework for on-road vehicle recognition and tracking,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 267–276, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Tehrani Niknejad, A. Takeuchi, S. Mita, and D. McAllester, “On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 748–758, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. C. R. Wang and J. J. Lien, “Automatic vehicle detection using local features—a statistical approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 1, pp. 83–96, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. 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, San Antonio, Tex, USA, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Jazayeri, H. Cai, J. Y. Zheng, and M. Tuceryan, “Vehicle detection and tracking in car video based on motion model,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 583–595, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. W. H. Li, H. Y. Ni, Y. Wang, B. Fu, P. X. Liu, and S. J. Wang, “Detection of partially occluded pedestrians by an enhanced cascade detector,” IET Intelligent Transport Systems, 2014. View at Publisher · View at Google Scholar
  15. S. Ali and M. Shah, “A supervised learning framework for generic object detection in images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 2, pp. 1347–1354, San Diego, Calif, USA, June 2005.
  16. J. Gall and V. Lempitsky, “Class-specific hough forests for object detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 1022–1029, Miami, Fla, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Sun, H. Gao, and O. Kaynak, “Adaptive backstepping control for active suspension systems with hard constraints,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 3, pp. 1072–1079, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. W. Sun, Z. Zhao, and H. Gao, “Saturated adaptive robust control for active suspension systems,” IEEE Transactions on Industrial Electronics, vol. 60, no. 9, pp. 3889–3896, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. W. H. Sun, H. J. Gao, and B. Yao, “Adaptive robust vibration control of full-car active suspensions with electrohydraulic actuators,” IEEE Transactions on Control Systems Technology, vol. 21, no. 6, pp. 2417–2422, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. W. Sun, H. Gao Sr., and O. Kaynak, “Finite frequency H control for vehicle active suspension systems,” IEEE Transactions on Control Systems Technology, vol. 19, no. 2, pp. 416–422, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. G. D. Tian, M. C. Zhou, and J. W. Chu, “A chance constrained programming approach to determine the optimal disassembly sequence,” IEEE Transactions on Automation Science and Engineering, vol. 10, no. 4, pp. 1004–1013, 2013. View at Google Scholar
  22. G. D. Tian, M. C. Zhou, J. W. Chu, and Y. M. Liu, “Probability evaluation models of product disassembly cost subject to random removal time and different removal labor cost,” IEEE Transactions on Automation Science and Engineering, vol. 9, no. 2, pp. 288–295, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. G. Tian, J. Chu, Y. Liu, H. Ke, X. Zhao, and G. Xu, “Expected energy analysis for industrial process planning problem with fuzzy time parameters,” Computers and Chemical Engineering, vol. 35, no. 12, pp. 2905–2912, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. Z. Y. Wang and G. J. Klir, Fuzzy Measure Theory, Plenum Press, New York, NY, USA, 1992. View at Publisher · View at Google Scholar · View at MathSciNet
  25. Y. Wang and W. Li, “High-precision video flame detection algorithm based on multi-feature fusion,” Journal of Jilin University, vol. 40, no. 3, pp. 769–775, 2010. View at Google Scholar · View at Scopus
  26. Y. Ding, W. H. Li, J. T. Fan, and H. M. Yang, “A moving object detection algorithm base on choquet integrate,” Acta Electronica Sinica, vol. 38, no. 2, pp. 263–268, 2010. View at Google Scholar · View at Scopus
  27. “Caltech Cars (Rear),” http://www.vision.caltech.edu/html-files/archive.html.
  28. M. B. Qi, Y. Pan, and Y. X. Zhang, “Preceding moving vehicle detection based on shadow of chassis',” Journal of Electronic Measurement and Instrument, vol. 26, no. 1, pp. 54–59, 2012. View at Google Scholar
  29. Q. Zhu, S. Avidan, M. C. Ye, and K. T. Cheng, “Fast human detection using a cascade of Histograms of Oriented Gradients',” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), New York, NY, USA, 2006.
  30. K. Tieu and P. Viola, “Boosting image retrieval,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), pp. 228–235, Hilton Head Island, SC, USA, June 2000. View at Publisher · View at Google Scholar