Approaches of Computing Traffic Load for Automated Traffic Signal Control: A Survey

  • Pratishtha Gupta
  • G. N. Purohit
  • Adhyana Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


Traffic images captured using CCTV camera can be used to compute traffic load. This document presents a survey of the research works related to image processing, traffic load, and the technologies used to re-solve this issue. Results of the implementation of two approaches: morphology-based segmentation and edge detection using sobel operator, which are close to traffic load computation have been shown. Segmentation is the process of partitioning a digital image into its constituent parts or objects or regions. These regions share common characteristics based on color, intensity, texture, etc. The first step in image analysis is to segment an image based on discontinuity detection technique (Edge-based) or similarity detection technique (Region-based). Morphological operators are tools that affect the shape and boundaries of regions in the image. Starting with dilation and erosion, the typical morphological operation involves an image and a structure element. The edge detection consists of creating a binary image from a grayscale image where the pixels in the binary image are turned off or on depending on whether they belong to region boundaries or not. Image processing is considered as an attractive and flexible technique for automatic analysis of road traffic scenes for the measurement and data collection of road traffic parameters. Combined background differencing and edge detection and segmentation techniques are used to detect vehicles and measure various traffic parameters. Real-time measurement and analysis of road traffic flow parameters such as volume, speed and queue are increasingly required for traffic control and management.


Image processing Simulation Segmentation Edge detection Real time Traffic load computation 


  1. 1.
    Chien, S.-Y., Chen, L.-G.: Reconfigurable Morphological Image Processing Accelerator for Video Object Seg-mentation. Signal. Process. Syst. 62(1), 77–96 (2011)CrossRefGoogle Scholar
  2. 2.
    Thakur, R.R., Dixit, S.R., Dr.Deshmukh, A.Y.: VHDL design for image segmentation using gabor filter for disease detection. Int. J. VLSI Design. Commun. Sys. 3(2), 211 (2012).Google Scholar
  3. 3.
    Ramadevi, Y., Sridevi, T., Poornima, B., Kalyani, B.: Segmentation and object recognition using edge detection techniques. Int. J. Comp. Sci. Info. Technol. 2(6), 153–161 (2010)Google Scholar
  4. 4.
    Al-amri, S.S., Kalyankar, N.V., Khamitkar, S.D.: Image segmentation by using thershod techniques. J. Comput. 2(5), ISSN 2151–9617 (2010).Google Scholar
  5. 5.
    Peng, B., Zhang, L., Zhang, D.: Automatic image segmentation by dynamic region merging. The Hong Kong Polytechnic University, Hong Kong (2010)Google Scholar
  6. 6.
    Papasaika-Hanusch, H.: Digital image processing using matlab. ETH Zurich, Zurich (1967)Google Scholar
  7. 7.
    Mrs. Allin Christe, S., Mr. Vignesh, M., Dr. Kandaswamy, A.: An efficient FPGA implementation of MRI image filtering and tumour characterization using Xilinx system generator. Int. J. VLSI. Des. Comm. Sys. 2(4), (2011).Google Scholar
  8. 8.
    Draper, B.A.: Ross Beveridge, J., Willem Böhm, A.P., Ross, C., Chawathe, M.: Accelerated image processing on FPGAs. IEEE Trans. Image Process. 12(12), 1543–1551 (2003)CrossRefGoogle Scholar
  9. 9.
    Sriramakrishnan, C., Shanmugam, A.: Image Retrieval Optimization Using FPGA Based Fuzzy Segmentation, ISSN 1450–216X 63(1) (2011).Google Scholar
  10. 10.
    Gupta, P., Purohit, G.N., Dadhich, A.: Approaches for intelligent traffic system: a survey. Banastahli University, Jaipur (2012)Google Scholar
  11. 11.
    Duan, T.D., Du Hong, T.L., Phuoc, T.V.: Hoang. Building an automatic vehicle license- plate recognition system. Int. J. Adv. technol, N.V. (2005)Google Scholar
  12. 12.
    Abhijit Mahalanobis, Jamie Cannon, S. Robert, Stanfill, Robert Muise, Lockheed Martin, Network video image pro-cessing for security, Surveillance, and Situational Awareness. Digital. Wireless. Commun. doi:10.1117/12.548981.Google Scholar
  13. 13.
    Siyal, M.Y., Fathi, M., Atiquzzaman, M.: A parallel pipeline based multiprocessor system for real-time measurement of road traffic parameters. Int. J. Imaging. Sys. Technol. 21(3), 260–270 (2011)Google Scholar
  14. 14.
    Kastrinaki, V., Zervakis, M., Kalaitzakis, K.: A survey of video processing techniques for traffic applications. Image. Vision. Comput. 21, 359–381 (2003)CrossRefGoogle Scholar
  15. 15.
    Koutsia, A., Semertzidi1, T., Dimitropoulos, K., Grammalidis, N.: Intelligent traffic monitoring and surveillance with multiple camras. In: Proceedings of International Workshop on Content-Based Multimedia Indexing (CBMI ’08), 125–132 (2008).Google Scholar
  16. 16.
    Ejaz, Z.: Morphological image processing based road traffic signal control system.Google Scholar
  17. 17.
    Bosman, J.: Traffic loading characteristics of south african heavy vehicles.Google Scholar
  18. 18.
    Parker, S.: Ladeji-Osias. Implementing a histogram equalization algorithm in reconfigurable hardware, J.K. (2009)Google Scholar
  19. 19.
    Ms. Chikkali, P S.: FPGA based Image edge detection and segmentation. Int. J. Adv. Eng. Sci. 9(2), 187–192 (2011).Google Scholar
  20. 20.
    Ali, S.M., Mr. Naveen, Mr. Khayum.: FPGA based design and implementation of image architecture using XILINX system generator, IJCAE, 3(1), 132–138 (2012).Google Scholar
  21. 21.
    Elamaran, V., Rajkumar, G.: FPGA implementation of point processes using Xilinx system generator 41(2), (2012).Google Scholar
  22. 22.
    Chandrashekar, M., Naresh Kumar, U., Sudershan Reddy, K., Nagabhushan Raju, K.: FPGA implementation of high speed In: Frared Image Enhancement, ISSN 0975–6450 1(3), 279–285 (2009).Google Scholar
  23. 23.
    Acharya, A., Mehra, R., Takher, V.S.: FPGA based non uniform illumination correction in image processing applications Int. J. Comp. Tech. Appl. 2(2), 349–358 (2009)Google Scholar
  24. 24.
    Gribbon, K. T., Bailey, D. G., Johnston, C.T.: Design patterns for image Processing Algorithm Development on FPGAs.Google Scholar
  25. 25.
    Devika, S.V., Khumuruddeen, S.K., Alekya.: Hardware implementation of Linear and Morphological Image Processing on FPGA. 2(1), 645–650 (2012).Google Scholar
  26. 26.
    Anusha, G., Dr.JayaChandra Prasad, T., Dr.Satya Narayana, D.: Implementation of SOBEL edge detection on FPGA. 3(3) (2012).Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Pratishtha Gupta
    • 1
  • G. N. Purohit
    • 1
  • Adhyana Gupta
    • 1
  1. 1.Banasthali UniversityBanasthaliIndia

Personalised recommendations