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

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Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 236))

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Abstract

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.

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References

  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)

    Article  Google Scholar 

  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. 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. 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. Peng, B., Zhang, L., Zhang, D.: Automatic image segmentation by dynamic region merging. The Hong Kong Polytechnic University, Hong Kong (2010)

    Google Scholar 

  6. Papasaika-Hanusch, H.: Digital image processing using matlab. ETH Zurich, Zurich (1967)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  9. Sriramakrishnan, C., Shanmugam, A.: Image Retrieval Optimization Using FPGA Based Fuzzy Segmentation, ISSN 1450–216X 63(1) (2011).

    Google Scholar 

  10. Gupta, P., Purohit, G.N., Dadhich, A.: Approaches for intelligent traffic system: a survey. Banastahli University, Jaipur (2012)

    Google Scholar 

  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. 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. 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. Kastrinaki, V., Zervakis, M., Kalaitzakis, K.: A survey of video processing techniques for traffic applications. Image. Vision. Comput. 21, 359–381 (2003)

    Article  Google Scholar 

  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. Ejaz, Z.: Morphological image processing based road traffic signal control system.

    Google Scholar 

  17. Bosman, J.: Traffic loading characteristics of south african heavy vehicles.

    Google Scholar 

  18. Parker, S.: Ladeji-Osias. Implementing a histogram equalization algorithm in reconfigurable hardware, J.K. (2009)

    Google Scholar 

  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. 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. Elamaran, V., Rajkumar, G.: FPGA implementation of point processes using Xilinx system generator 41(2), (2012).

    Google Scholar 

  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. 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. Gribbon, K. T., Bailey, D. G., Johnston, C.T.: Design patterns for image Processing Algorithm Development on FPGAs.

    Google Scholar 

  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. Anusha, G., Dr.JayaChandra Prasad, T., Dr.Satya Narayana, D.: Implementation of SOBEL edge detection on FPGA. 3(3) (2012).

    Google Scholar 

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Authors and Affiliations

  1. Banasthali University, Banasthali, Rajasthan, India

    Pratishtha Gupta, G. N. Purohit & Adhyana Gupta

Authors
  1. Pratishtha Gupta
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  2. G. N. Purohit
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  3. Adhyana Gupta
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Corresponding author

Correspondence to Pratishtha Gupta .

Editor information

Editors and Affiliations

  1. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    B. V. Babu

  2. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  5. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  6. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Kanad Ray

  7. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Umesh Gupta

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Gupta, P., Purohit, G.N., Gupta, A. (2014). Approaches of Computing Traffic Load for Automated Traffic Signal Control: A Survey. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_99

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  • DOI: https://doi.org/10.1007/978-81-322-1602-5_99

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1601-8

  • Online ISBN: 978-81-322-1602-5

  • eBook Packages: EngineeringEngineering (R0)

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