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Traffic Density Analysis based on Image Segmentation with Adaptive Threshold

Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Luong Anh Tuan Nguyen, Thi-Ngoc-Thanh Nguyen

Luong Anh Tuan Nguyen and Thi-Ngoc-Thanh Nguyen. Traffic Density Analysis based on Image Segmentation with Adaptive Threshold. Communications on Applied Electronics 7(19):1-7, August 2018. BibTeX

	author = {Luong Anh Tuan Nguyen and Thi-Ngoc-Thanh Nguyen},
	title = {Traffic Density Analysis based on Image Segmentation with Adaptive Threshold},
	journal = {Communications on Applied Electronics},
	issue_date = {August 2018},
	volume = {7},
	number = {19},
	month = {Aug},
	year = {2018},
	issn = {2394-4714},
	pages = {1-7},
	numpages = {7},
	url = {},
	doi = {10.5120/cae2018652780},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Traffic congestion has become an important problem in recent years. The main reason is the increase in the population in big cities and respective increase in number of vehicles. Traffic jams not only affect the human routine lives but also lead to a rise in the cost of transportation. So, an automatic traffic control system is required to manage the traffic congestion problem. The traffic density analysis will support the traffic management problems such as intelligent traffic signal control, traffic planning, etc. This paper has proposed a traffic density analysis method based on image segmentation with adaptive threshold. The system was designed and evaluated with the traffic images taken in Ho Chi Minh City, Viet Nam. The proposed method provides a accuracy analysis rate higher than 97% and a verification error lower than 3%.


  1. Pratishtha Gupta , Purohit , Adhyana Gupta,Traffic Load Computation using Matlab Simulink Model Blockset International Journal of Advanced Research in Computer and Communication EngineeringVol. 2, Issue 6, June 2013
  2. Atkociunas, Blake, Juozapavicius Kazimianec Image processing in road traffic analysis Nonlinear Analysis: Modeling andControl, 2005, Vol. 10, No. 4, 315332
  3. Bharti Sharma, Vinoth Kumar Katiyar, Aravind Kumar Gupta, and Akansha Singh.(2014) The Automated Vehicle Detection of Highway Traffic images by Differential Morphological Profile. Journal of Transportation Technologies,4,150-156.
  4. Dharani.S.J, Anitha.V, Traffic Density Count by Optical Flow Algorithm using Image Processing,Automative Parts system and Application, ISSN 2347-6710(paper) Volume 3,Special Issue 2. April 2014.
  5. Ozkurt C, Camci F. Automatic traffic density estimation and vehicle classification for traffic surveillance systems using Neural Networks. Mathematical and Computational Applications. 2009; 14(3):18796.
  6. C. Stuiz, T. A. Runkler, Classification and Predicts of Road Traffic using Application Specific Fuzzy Clustering, Fuzzy Systems, IEEE Transactions, pp. 297-308, 2002.
  7. Luong Anh Tuan Nguyen and Thi-Ngoc-Thanh Nguyen. Traffic Image Classification using Horizontal Slice Algorithm. International Journal of Computer Applications 148(11):30-34, August 2016.
  8. Luong Anh Tuan Nguyen and Thi-Ngoc-Thanh Nguyen. Traffic Density Identification based on Neural Network and Histogram. International Journal of Computer Applications 172(9):8-13, August 2017.
  9. Al Bovik, Handbook of Image and Video Processing, Academic Press, 2000.
  10. Gonzalez, R., C., and Woods, R., E., 2001, Digital Image Processing, Prentice Hall, NJ, 2001
  11. Luong Anh Tuan Nguyen, Huu Khuong Nguyen. Traffic Density Identification Based On Histogram. Journal of Transportation Science and Technology, ISSN: 1859-4263, Vol 15-05/2015, pp 23-27.
  12. C. C. Sun. S. J. Ruan, M. C. Shie, T.W. Pai, Dynamic Contrast Enhancement based on Histogram Specification, IEEE Transactions on Consumer Electronics, 51(4), pp.1300- 1305, 2005.
  13. Xiangyun Ye, Mohamed Cheriet, Senior Member, Ching Y. Suen (2001), Stroke-Model-Based Character Extraction from Gray-Level Document Images, IEEE, 2001.
  14. Binary Image (June, 2018), https : ==en:wikipedia:org=wiki=Binaryimage
  15. Otsu, N., ”A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics , Vol. 9, No. 1, 1979, pp. 62-66.
  16. T. Bouwmans. Traditional and recent approaches in background modeling for foreground detection: An overview. Computer Science Review , 1112:31 66, 2014.
  17. Nida M. Zaitouna, Musbah J. Aqel. (2015). Survey on Image Segmentation Techniques. International Conference on Communication, Management and Information Technology (ICCMIT). pp 797 806.
  18. C. Willmott, and K. Matsuura, Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in assessing average model performance, Clim. Res., 30, 7982, 2005.


Traffic Density, Otsu threshold, Image Segmentation, Histogram