Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

A Video Traffic Flow Detection System Based on Machine Vision

  • Wang, Xin-Xin (School of Computer and Information Engineering, Luoyang Institute of Science and Technology) ;
  • Zhao, Xiao-Ming (School of Electrical and Automation, Luoyang Institute of Science and Technology) ;
  • Shen, Yu (School of Electronic and Information Engineering, Lanzhou Jiaotong University)
  • Received : 2017.10.11
  • Accepted : 2019.04.22
  • Published : 2019.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

Keywords

References

  1. T. Tan, W. Wang, J. Wan, Y. Zhou, and B. Xie, "Research of traffic flow detecting system based on multiple loop detectors," Journal of East China Jiaotong University, vol. 34, no. 2, pp. 60-65, 2017.
  2. T. Zhao, X. Jiang, and H. Xie, "Development of intelligent ultrasonic flow-detecting system based on 89C51 single chip microcomputer," Instrument Technique and Sensor, vol. 1999, no. 8, pp. 29-32, 1999.
  3. J. Xu, F. Q. Hu, and H. Huo, "Vehicle detection and segmentation method based on two-dimensional spacetime image analysis," Journal of Shanghai Jiaotong University, vol. 36, no. 6, pp. 887-890, 2011. https://doi.org/10.3321/j.issn:1006-2467.2002.06.035
  4. X. P. Ji, "Study on video-based detection, recognition and tracking method of vehicle in ITS," Ocean University of China, Shandong, 2006.
  5. C. F. Liu, B. L. Hu, C. L. Wang, and L. Liu, "The redevelopment of Matrox meteor-II/digital image grabbing board with VC++," Advanced Materials Research, vol. 532, pp. 1152-1156, 2012. https://doi.org/10.4028/www.scientific.net/amr.532-533.1152
  6. Y. Yuan, Z. Xiong, and Q. Wang, "An incremental framework for video-based traffic sign detection, tracking, and recognition," IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 7, pp. 1918-1929, 2017. https://doi.org/10.1109/TITS.2016.2614548
  7. J. Liu, F. Luo, H. Huang, Y. Liu. "Ear detection based on improved CamShift and AdaBoost algorithm," Journal of Computational Information Systems, vol. 10, no. 13, pp. 5619-5626, 2014.
  8. M. Ibrahim, M. Riad, and M. El-Abd, "RoadEye: the intelligent transportation system," in Proceedings of 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Hammamet, Tunisia, 2017, pp. 21-22.
  9. N. Khairdoost, S. A. Monadjemi, Z. Davarzani, and K. Jamshidi, "GA based PHOG-PCA feature weighting for on-road vehicle detection," International Journal of Information and Electronics Engineering, vol. 3, no. 1, pp. 104-108, 2013.
  10. I. Ali, A. Malik, W. Ahmed, and S. A. Khan, "Real-time vehicle recognition and improved traffic congestion resolution," in Proceedings of 2015 13th International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, pp. 228-233.
  11. H. Shi, "Geometry-aware traffic flow analysis by detection and tracking," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, 2018, pp. 116-120.
  12. R. T. Collins and Y. Liu, "On-line selection of discriminative tracking features," in Proceedings of 9th IEEE International Conference on Computer Vision, Nice, France, 2003, pp. 346-352.
  13. H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line boosting," in Proceedings of the British Machine Vision Conference, Edinburgh, UK, 2006, pp. 1-10.
  14. J. Trefny and J. Matas, "Extended set of local binary patterns for rapid object detection," in Proceedings of Computer Vision Winter Workshop, Nove Hrady, Czech Republic, 2010, pp. 37-43.
  15. M. Hajiabadi, B. Razeghi, and M. Mir, "A novel adaptive algorithm for estimation of sparse parameters in non- Gaussian noise," in Proceedings of 2015 International Conference and Workshop on Computing and Communication (IEMCON), Vancouver, Canada, 2015, pp. 1-5.
  16. C. Stauffer and W. E. L. Grimson, "Adaptive background mixture models for real-time tracking," in Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), Fort Collins, CO, 1999, pp. 246-252.
  17. A. Tavakkoli, M. Nicolescu, G. Bebis, and M. Nicolescu, "Non-parametric statistical background modeling for efficient foreground region detection," Machine Vision and Applications, vol. 20, no. 6, pp. 395-409, 2009. https://doi.org/10.1007/s00138-008-0134-2
  18. J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, "Hierarchical method for foreground detection using codebook model," IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 6, pp. 804-815, 2011. https://doi.org/10.1109/TCSVT.2011.2133270
  19. S. Surkutlawar and R. K. Kulkarni, "Shadow suppression using RGB and HSV color space in moving object detection," International Journal of Advanced Computer Science and Applications, vol. 4, no. 1, pp. 164-169, 2013.
  20. D. K. Appana, R. Islam, S. A. Khan, and J. M. Kim, "A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems," Information Sciences, vol. 418-419, pp. 91-101, 2017. https://doi.org/10.1016/j.ins.2017.08.001
  21. N. Martel-Brisson and A. Zaccarin, "Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation," in Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, 2008, pp. 1-8.
  22. J. Choi, Y. J. Yoo, and J. Y. Choi, "Adaptive shadow estimator for removing shadow of moving object," Computer Vision and Image Understanding, vol. 114, no. 9, pp. 1017-1029, 2010. https://doi.org/10.1016/j.cviu.2010.06.003
  23. A. Prati, I. Mikic, M. M. Trivedi, and R. Cucchiara, "Detecting moving shadows: algorithms and evaluation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 918-923, 2003. https://doi.org/10.1109/TPAMI.2003.1206520
  24. I. Mikic, P. C. Cosman, G. T. Kogut, and M. M. Trivedi, "Moving shadow and object detection in traffic scenes," in Proceedings 15th International Conference on Pattern Recognition, Barcelona, Spain, 2000, pp. 321-324.
  25. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. 5, pp. 564-575, 2003. https://doi.org/10.1109/TPAMI.2003.1195991
  26. A. Broggi, S. Debattisti, M. Panciroli, P. Grisleri, E. Cardarelli, M. Buzzoni, and P. Versari, "High performance multi-track recording system for automotive applications," International Journal of Automotive Technology, vol. 13, article no. 123, 2012.
  27. R. Mukundan and K. R. Ramakrishnan, Moment Functions in Image Analysis: Theory and Applications. Singapore: World Scientific Publishing, 1998.
  28. M. K. Hu, "Visual pattern recognition by moment invariants," IRE Transactions on Information Theory, vol. 8, no. 2, pp. 179-187, 1962. https://doi.org/10.1109/TIT.1962.1057692