Vehicle counting application utilizing background subtraction method with large-scale camera data


  • Mien Puoc Doan Tra Vinh University image/svg+xml
  • Vu The Tran University of Da Nang image/svg+xml
  • Sy Ngo Van Vietnam Research Institute of Electronics, Informatics and Automation



Large-scale camera, background subtraction, Da Nang City


In modern society, people are increasingly using cameras at home, in shops, and on the streets. Traffic systems have also invested in building more surveillance camera systems. The data collected by cameras contains valuable information for traffic regulation and recording traffic violations. The challenge is how to effectively use this data. In this article, we will discuss the use of real-time data from surveillance cameras on some roads in Da Nang City for vehicle counting using background subtraction methods. Additionally, we also tested the detection of red light violations to contribute to the development of a smart traffic system. So, the use of background subtraction in analyzing real-time data from surveillance cameras can greatly improve traffic management


Download data is not yet available.


Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., & Darrell, T. (2018). Bdd100k: A diverse driving video database with scalable annotation tooling. arXiv preprint arXiv:1805.04687, 2(5), 6.

S. Jain, V. Nguyen, M. Gruteser, and P. Bahl, “Panoptes: servicing multiple applications simultaneously using steerable cameras.” in IPSN, 2017, pp. 119–130. DOI:

S. Zhang, G. Wu, J. P. Costeira, and J. M. Moura, “Understanding traffic density from large-scale web camera data,” arXiv preprint arXiv:1703.05868, 2017. DOI:

V. Lempitsky and A. Zisserman, “Learning to count objects in images,” in Advances in neural information processing systems, 2010, pp. 1324–1332.

M. Bayly, M. Regan, and S. Hosking, “Intelligent transport systems and motorcycle safety” Prevention, vol. 28, pp. 325–332, 2006.

J. Sun and J. Sun, “A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data,” Transportation Research Part C: Emerging Technologies, vol. 54, pp. 176–186, 2015. DOI:

Jain, N. K., Saini, R. K., & Mittal, P. (2019). A review on traffic monitoring system techniques. Soft computing: Theories and applications: Proceedings of SoCTA 2017, 569-577. DOI:

R. Tina and S. G. Sharmila, “Density based traffic signal system,” International Journal and Magazine of engineering Technology Management and Research, vol. 2, no. 9, pp. 149–151, 2015.

N. G. Narole and P. R. Bajaj, “A neurogenetic system design for monitoring driver’s fatigue: A design approach,” in 2008 First International Conference on Emerging Trends in Engineering and Technology. IEEE, 2008, pp. 711–714. DOI:

M. Hofmann, P. Tiefenbacher, and G. Rigoll, “Background segmentation with feedback: The pixel-based adaptive segmenter,” in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 2012, pp. 38–43. DOI:

P. G. Michalopoulos, “Vehicle detection video through image processing: the autoscope system,” IEEE Transactions on vehicular technology, vol. 40, no. 1, pp. 21–29, 1991. DOI:

Q. Cai, A. Mitiche, and J. K. Aggarwal, “Tracking human motion in an indoor environment,” in Proceedings., International Conference on Image Processing, vol. 1. IEEE, 1995, pp. 215–218.

D. G. Lowe, “Distinctive image features from scale-invariant key-points,” International journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004. DOI:

D. A. Forsyth and J. Ponce, “A modern approach,” Computer vision: a modern approach, vol. 17, pp. 21–48, 2003.

A. H. Lai, G. S. Fung, and N. H. Yung, “Vehicle type classification from visual-based dimension estimation,” in ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585). IEEE, 2001, pp. 201–206.

M. Piccardi, “Background subtraction techniques: a review,” in 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), vol. 4. IEEE, 2004, pp. 3099–3104.

T. Bouwmans, “Traditional and recent approaches in background modeling for foreground detection: An overview,” Computer science review, vol. 11, pp. 31–66, 2014. DOI:




How to Cite

M. P. Doan, V. T. Tran, and S. N. Van, “Vehicle counting application utilizing background subtraction method with large-scale camera data”, EAI Endorsed Trans Smart Cities, vol. 7, no. 3, Apr. 2024.