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Vehicle Detection and Counting Using Adaptive Background Model Based on Approximate Median Filter and Triangulation Threshold Techniques

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Abstract

Background subtraction method is widely used for vehicle detection. One of the issues in this method is to find a suitable and accurate background model that works in all conditions. Moreover, setting an appropriate threshold value to discriminate between the moving objects and stationary background plays a crucial role in increasing the detection performance. In this paper, an adaptive background model combined with an adaptive threshold method is proposed. It is demonstrated that the proposed method can efficiently differentiate between moving vehicles and background in urban roads under different weather conditions (i.e., normal, rainy, foggy, and snowy). Comparisons between the proposed method and other methods, such as the adaptive local threshold (ALT) and the three frame-differencing methods show the potential of our approach. The experimental results show that the proposed method increases the average recall value by 16.4% and the average precision value by 21.7% in comparison to the ALT method with a negligible increase in the processing time.

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REFERENCES

  1. Asaidi, H., Aarab, A., and Bellouki, M., Shadow elimination and vehicles classification approaches in traffic video surveillance context, J. Visual Lang. Comput., 2014, vol. 25, no. 4, pp. 333–345.

    Article  Google Scholar 

  2. Ganesh Raghtate and Abhilasha K. Tiwari, Moving object counting in video signals, Int. J. Eng. Res. Gen. Sci., 2014, vol. 2, no. 3, pp. 2091–2730.

    Google Scholar 

  3. Honghong Yang and Shiru Qu, Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition, IET Intell. Transp. Syst., 2017, vol. 12, no. 1, pp. 75–85.

    Article  Google Scholar 

  4. Yang Wang, Joint random field model for all-weather moving vehicle detection, IEEE Trans. Image Process., 2010, vol. 19, no. 9, pp. 2491–2501.

    Article  MathSciNet  Google Scholar 

  5. Mandellos, N.A., Keramitsoglou, I., and Kiranoudis, C.T., A background subtraction algorithm for detecting and tracking vehicles, Expert Syst. Appl., 2011, vol. 38, no. 3, pp. 1619–1631.

    Article  Google Scholar 

  6. Buch, N., Velastin, S.A., and Orwell, J., A review of computer vision techniques for the analysis of urban traffic, IEEE Trans. Intell. Transp. Syst., 2011, vol. 12, no. 3, pp. 920–939.

    Article  Google Scholar 

  7. Sayanan Sivaraman and Mohan Manubhai Trivedi, Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis, IEEE Trans. Intell. Transp. Syst., 2013, vol. 14, no. 4, pp. 1773–1795.

    Article  Google Scholar 

  8. Nunes, E., Conci, A., and Sanchez, A., Robust background subtraction on traffic videos, Proc. IEEE 18th International Conference on Systems, Signals and Image Processing, 2011, pp. 1–4.

  9. Robert, K., Video-based traffic monitoring at day and night vehicle features detection tracking, Proc. IEEE 12th International Conference on Intelligent Transportation Systems, 2009, pp. 1–6.

  10. Atibi Mohamed, Atouf Issam, Boussaa Mohamed, and Bennis Abdellatif, Real-time detection of vehicles using the Haar-like features and artificial neuron networks, Procedia Comput. Sci., 2015, vol. 73, pp. 24–31.

    Article  Google Scholar 

  11. Bing-Fei Wu and Jhy-Hong Juang, Adaptive vehicle detector approach for complex environments, IEEE Trans. Intell. Transp. Syst., 2012, vol. 13, no. 2, pp. 817–827.

    Article  Google Scholar 

  12. Shuguang Li, Hongkai Yu, Jingru Zhang, Kaixin Yang, and Ran Bin, Video-based traffic data collection system for multiple vehicle types, IET Intell. Transp. Syst., 2013, vol. 8, no. 2, pp. 164–174.

    Article  Google Scholar 

  13. Luo-Wei Tsai, Jun-Wei Hsieh, and Kuo-Chin Fan, Vehicle detection using normalized color and edge map, IEEE Trans. Image Process., 2007, vol. 16, no. 3, pp. 850–864.

    Article  MathSciNet  Google Scholar 

  14. Zack, G.W., Rogers, W.E., and Latt, S.A., Automatic measurement of sister chromatid exchange frequency, J. Histochem. Cytochem., 1977, vol. 25, no. 7, pp. 741–753.

    Article  Google Scholar 

  15. Image Sequence Server at Universität Karlsruhe. http://i21www.ira.uka.de/image_sequences. Accessed April 22, 2019.

  16. Parks, D.H. and Fels, S.S., Evaluation of background subtraction algorithms with post-processing, Proc. IEEE 5th International Conference on Advanced Video and Signal Based Surveillance, 2008, pp. 192–199.

  17. Jie Zhou, Dashan Gao, and David Zhang, Moving vehicle detection for automatic traffic monitoring, IEEE Trans. Veh. Technol., 2007, vol. 56, no. 1, pp. 51–59.

    Article  Google Scholar 

  18. Lipton, A.J., Hironobu Fujiyoshi, and Patil, R.S., Moving target classification and tracking from real-time video, Proc. IEEE 4th workshop on Applications of Computer Vision, 1998, pp. 8–14.

  19. Niluthpol Chowdhury Mithun, Nafi Ur Rashid, and Mahbubur Rahman, S.M., Detection and classification of vehicles from video using multiple time-spatial images, IEEE Trans. Intell. Transp. Syst., 2012, vol. 13, no. 3, pp. 1215–1225.

    Article  Google Scholar 

  20. Ruolin Zhang and Jian Ding, Object tracking and detecting based on adaptive background subtraction, Procedia Eng., 2012, vol. 29, pp. 1351–1355.

    Article  Google Scholar 

  21. Haiying Zhang and Kun Wu, A vehicle detection algorithm based on three-frame differencing and background subtraction, Proc. IEEE 5th International Symposium on Computational Intelligence and Design, 2012, pp. 148–151.

  22. Mau-Tsuen Yang, Rang-Kai Jhang, and Jia-Sheng Hou, Traffic flow estimation and vehicle-type classification using vision-based spatial-temporal profile analysis, IET Comput. Vision, 2013, vol. 7, no. 5, pp. 394–404.

    Article  Google Scholar 

  23. Yao Lin, Ma Fang, and Duan Shihong, An object reconstruction algorithm for moving vehicle detection based on three-frame differencing, Proc. IEEE 15th Int. Conf. on Scalable Computing and Communications and Its Associated Workshops, 2015, pp. 1864–1868.

  24. McFarlane, N.J.B. and Schofield, C.P., Segmentation and tracking of piglets in images, Mach. Vision Appl., 1995, vol. 8, no. 3, pp. 187–193.

    Article  Google Scholar 

  25. Mohamed A. El-Khoreby and Syed Abd Rahman Abu-Bakar, Vehicle detection and counting for complex weather conditions, Proc. IEEE International Conference on Signal and Image Processing Applications, 2017, pp. 425–428.

  26. A video database for testing changes detection algorithms. http://changedetection.net/. Accessed April 22, 2019.

  27. Yizhong Yang, Qiang Zhang, Pengfei Wang, Xionglou Hu, and Nengju Wu, Moving object detection for dynamic background scenes based on spatiotemporal model, Adv. Multimedia, 2017, vol. 2017.

  28. Mohammad Mahdi Moghimi, Maryam Nayeri, Majid Pourahmadi, and Mohammad Kazem Moghimi, Moving vehicle detection using AdaBoost and Haar-like feature in surveillance videos, Int. J. Imaging Rob., 2018, vol. 18, no. 1, pp. 94–106.

    Google Scholar 

  29. Nur, S.A., Ibrahim, M.M., Ali, N.M., and Nur, F.I.Y., Vehicle detection based on underneath vehicle shadow using edge features, Proc. IEEE 6th Int. Conf. on Control System, Computing and Engineering (ICCSCE), 2016, pp. 407–412.

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Funding

This work was supported by Universiti Teknologi Malaysia (UTM) and Ministry of Education (MoE) under Research University Grant Q.J130000.2523.19H86.

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Authors and Affiliations

  1. Department of Electronics and Communications, Arab Academy for Science Technology and Maritime Transport, Alexandria, Egypt

    M. A. El-Khoreby

  2. School of Electrical Engineering, Faculty of Engineering Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    M. A. El-Khoreby, S. A. R. Abu-Bakar & M. Mohd Mokji

  3. Faculty of Science and Technology Universiti Sains Islam Malaysia, 71800, Nilai, Malaysia

    S. N. Omar

Authors
  1. M. A. El-Khoreby
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  2. S. A. R. Abu-Bakar
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  3. M. Mohd Mokji
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Correspondence to M. A. El-Khoreby.

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El-Khoreby, M.A., Abu-Bakar, S.A., Mokji, M.M. et al. Vehicle Detection and Counting Using Adaptive Background Model Based on Approximate Median Filter and Triangulation Threshold Techniques. Aut. Control Comp. Sci. 54, 346–357 (2020). https://doi.org/10.3103/S0146411620040057

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  • DOI: https://doi.org/10.3103/S0146411620040057

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