Skip to main content
SpringerLink
Log in

Front-view car detection and counting with occlusion in dense traffic flow

  • Regular Papers
  • Control Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

In dense traffic flow, car occlusion is usually one of the great challenges of vehicle detection and tracking in traffic monitoring systems. Current methods of car hypothesis such as symmetry or shadow based method work only with non-occluded cars. In this paper, we proposed an approach to car detection and counting using a new method of car hypothesis based on car windshield appearance which is the most feasible cue to hypothesize cars in occlusion situations. In hypothesis stage, Hough transformation is used to detect trapezoid-like regions where a car’s windshield could be located, and then candidate car regions are estimated by the windshield region and its size. In verification stage, HOG descriptor and a well-collected dataset are used to train a linear SVM classifier for detecting cars at a high accuracy rate. Then, a tracking process based on Kalman filter is used to track the movement of detected cars in consecutive frames of traffic videos, followed by rule-based reasoning for counting decision. Experimental results on real traffic videos showed that the system is able to detect, track and count multiple cars including occlusion in dense traffic flow in real-time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. N. Buch, S. A. Velastin, and J. Orwell, “A review of computer vision techniques for the analysis of urban traffic,” IEEE Trans. on Intelligent Transportation Systems, vol. 12, no. 3, pp. 920–939, 2011.

    Article  Google Scholar 

  2. H. Y. Zhao, O. Kim, J. S. Won, and D. J. Kang, “Lane detection and tracking based on annealed particle filter,” International Journal of Control Automation and Systems, vol. 12, no. 6, pp. 1303–1312, 2014.

    Article  Google Scholar 

  3. N. Ben Romdhane, M. Hammami, and H. Ben-Abdallah, A Lane Detection and Tracking Method for Driver Assistance System, Springer Berlin Heidelberg, 2011.

    Book  Google Scholar 

  4. V.-D. Hoang and K.-H. Jo, “Path planning for autonomous vehicle based on heuristic searching using online images,” Vietnam Journal of Computer Science, Open Access, 2014.

    Google Scholar 

  5. C. Kawatsu, J. Li, and C. J. Chung, Obstacle & Lane Detection and Local Path Planning for IGVC Robotic Vehicles Using Stereo Vision, Springer International Publishing, 2014.

    Book  Google Scholar 

  6. M. H. Le, V. D. Hoang, A. Vavilin, and K. H. Jo, “One-point-plus for 5-DOF localization of vehiclemounted omnidirectional camera in long-range motion,” International Journal of Control Automation and Systems, vol. 11, no. 5, pp. 1018–1027, 2013.

    Article  Google Scholar 

  7. T. Bin, Y. Qingming, G. Yuan, W. Kunfeng, and L. Ye, “Video processing techniques for traffic flow monitoring: a survey,” IEEE Conf. on Intelligent Transportation Systems, pp. 1103–1108, 2011.

    Google Scholar 

  8. T. Zielke, M. Brauckmann, and W. Vonseelen, “Intensity and edge-based symmetry detection with an application to car-following,” CVGIP-Image Understanding, vol. 58, no. 2, pp. 177–190, 1993.

    Article  Google Scholar 

  9. S. S. Teoh and T. Braunl, “Symmetry-based monocular vehicle detection system,” Machine Vision and Applications, vol. 23, no. 5, pp. 831–842, 2012.

    Article  Google Scholar 

  10. T. Luo-Wei, J.-W. Hsieh, and F. Kao-Chin, “Vehicle detection using normalized color and edge map,” Proc. of IEEE International Conf. on Image Processing, pp. II-598-601, 2005.

    Google Scholar 

  11. M. Betke, E. Haritaoglu, and L. S. Davis, “Realtime multiple vehicle detection and tracking from a moving vehicle,” Machine Vision and Applications, vol. 12, no. 2, pp. 69–83, 2000.

    Article  Google Scholar 

  12. C. Tzomakas and W. von Seelen, Vehicle Detection in Traffic Scenes Using Shadows, 1998.

    Google Scholar 

  13. T. Bucher, C. Curio, J. Edelbrunner, C. Igel, D. Kastrup, I. Leefken, G. Lorenz, A. Steinhage, and W. von Seelen, “Image processing and behavior planning for intelligent vehicles,” IEEE Trans. on Industrial Electronics, vol. 50, no. 1, pp. 62–75, 2003.

    Article  Google Scholar 

  14. C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, p. 252, 1999.

    Google Scholar 

  15. M. Boninsegna and A. Bozzoli, “A tunable algorithm to update a reference image,” Signal Processing-Image Communication, vol. 16, no. 4, pp. 353–365, 2000.

    Article  Google Scholar 

  16. J. R. Tyrer and L. M. Lobo, “An optical method for automated roadside detection and counting of vehicle occupants,” Proc. of the Institution of Mechanical Engineers Part D-Journal of Automobile Engineering, vol. 222, no. 5, pp. 765–774, 2008.

    Article  Google Scholar 

  17. Y. C. Kuo, N. S. Pai, and Y. F. Li, “Vision-based vehicle detection for a driver assistance system,” Computers & Mathematics with Applications, vol. 61, no. 8, pp. 2096–2100, 2011.

    Article  Google Scholar 

  18. C. Thou-Ho, L. Yu-Feng, and C. Tsong-Yi, “Intelligent vehicle counting method based on blob analysis in traffic surveillance,” Proc. of International Conf. on Innovative Computing, Information and Control, pp. 238–238, 2007.

    Google Scholar 

  19. T. Ying-Li, M. Lu, and A. Hampapur, “Robust and efficient foreground analysis for real-time video surveillance,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1182–1187 vol. 1181, 2005.

    Google Scholar 

  20. W. Bing-fei, L. Shin-Ping, and C. Yuan-Hsin, “A real-time multiple-vehicle detection and tracking system with prior occlusion detection and resolution,” Proc. of the Fifth IEEE International Symposium on Signal Processing and Information Technology, pp. 311–316, 2005.

    Google Scholar 

  21. H. Fujiyoshi and T. Kanade, “Layered detection for multiple overlapping objects,” IEICE Trans. on Information and Systems, Vol. E87d, no. 12, pp. 2821–2827, 2004.

    Google Scholar 

  22. R. S. Feris, B. Siddiquie, J. Petterson, Y. Zhai, A. Datta, L. M. Brown, and S. Pankanti, “Large-scale vehicle detection, indexing, and search in urban surveillance videos,” IEEE Trans. on Multimedia, vol. 14, no. 1, pp. 28–42, 2012.

    Article  Google Scholar 

  23. S. Gupte, O. Masoud, R. F. K. Martin, and N. P. Papanikolopoulos, “Detection and classification of vehicles,” IEEE Trans. on Intelligent Transportation Systems, vol. 3, no. 1, pp. 37–47, 2002.

    Article  Google Scholar 

  24. C. C. C. Pang, W. W. L. Lam, and N. H. C. Yung, “A method for vehicle count in the. presence of multiple-vehicle occlusions in traffic images,” IEEE Trans. on Intelligent Transportation Systems, vol. 8, no. 3, pp. 441–459, 2007.

    Article  Google Scholar 

  25. A. Ghasemi and R. Safabakhsh, “A real-time multiple vehicle classification and tracking system with occlusion handling,” Proc. of IEEE International Conf. on Intelligent Computer Communication and Processing, pp. 109–115, 2012.

    Google Scholar 

  26. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 886–893, 2005.

    Google Scholar 

  27. J. Canny, “A computational approach to edge detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986.

    Article  Google Scholar 

  28. R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,” Communications of the ACM, vol. 15, no. 1, pp. 11–15, 1972.

    Article  MATH  Google Scholar 

  29. V. F. Leavers, “Which Hough transform?,” CVGIP: Image Understanding, vol. 58, no. 2, pp. 250–264, 1993.

    Article  Google Scholar 

  30. Y. Furukawa and Y. Shinagawa, “Accurate and robust line segment extraction by analyzing distribution around peaks in Hough space,” Computer Vision and Image Understanding, vol. 92, no. 1, pp. 1–25, 2003.

    Article  Google Scholar 

  31. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010.

    Article  Google Scholar 

  32. V. D. Hoang, M. H. Le, and K. H. Jo, “Hybrid cascade boosting machine using variant scale blocks based HOG features for pedestrian detection,” Neurocomputing, vol. 135, no. 5, pp. 357–366, 2014.

    Article  Google Scholar 

  33. T. Gandhi and M. M. Trivedi, “Video based surround vehicle detection, classification and logging from moving platforms: issues and approaches,” Proc. of IEEE Intelligent Vehicles Symposium, pp. 1067–1071, 2007.

    Google Scholar 

  34. Open CV, Intel’s Computer Vision Library, Open Source, 2012.

    Google Scholar 

  35. K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, “An introduction to kernel-based learning algorithms,” IEEE Trans. on Neural Networks, vol. 12, no. 2, pp. 181–201, 2001.

    Article  Google Scholar 

  36. M. Ling, X. Mei, H. Yi, and Z. Yuefei, “Preceding vehicle detection using histograms of oriented gradients,” Proc. of International Conf. on Communications, Circuits and Systems, pp. 354–358, 2010.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, University of Ulsan, Daehak-ro 93, Nam-gu, Ulsan, 680-749, Korea

    Huy Van Pham & Byung-Ryong Lee

Authors
  1. Huy Van Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Byung-Ryong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Byung-Ryong Lee.

Additional information

Recommended by Associate Editor Kang-Hyun Jo under the direction of Editor Duk-Sun Shim.

This work was supported by the 2014 Research Fund of University of Ulsan, Korea.

Van Huy Pham received his B.S degree in Mathematics and Informatics from Can Tho University, Can Tho, Vietnam, in 2003, and his Master’s degree in Computer Science from University of Sciences, Ho Chi Minh City, Vietnam in 2007. In 2010, he joined Intelligent Mechatronics Laboratory to pursue a Ph.D. degree from the School of Mechatronics and Automotive Engineering, University of Ulsan, Ulsan, Korea. His current research interests include computer vision, image processing, machine learning, and intelligence transportation system.

Byung-Ryong Lee received his B.S. and M.S. degrees in Mechanical Engineering from Busan National University, Busan, Korea, in 1983 and 1988, respectively, and his Ph.D. degree in Mechanical Engineering from North Carolina State University, Raleigh, USA, in 1994. Since 1995, he has been with the Faculty of Engineering, University of Ulsan, Ulsan, Korea, where he is currently a Professor in Mechanical Engineering. His main research interests include robotics, especially bio-inspired robotics, fault detection and monitoring using machine-vision, and intelligent control application incorporating fuzzy, neural network, and genetic algorithm. Prof. Lee is a member of the Society of Mechanical Engineering of Korea (KSME), the Society of Precision Engineering of Korea (KSPE), and the Institute of Control, Robotics, and Systems (ICROS).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Van Pham, H., Lee, BR. Front-view car detection and counting with occlusion in dense traffic flow. Int. J. Control Autom. Syst. 13, 1150–1160 (2015). https://doi.org/10.1007/s12555-014-0229-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-014-0229-7

Keywords

Search

Navigation