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Extension of Cellular Automata for Dynamic Vehicle Tracking

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Abstract

Detecting and tracking moving vehicles in real-time traffic scenes are an emerging research area for intelligent transportation systems. This paper presents the computational model of Fuzzy Cellular Automata (FCA) to address the susceptible to environmental changes’ problem associated with the background subtraction methods for dynamic vehicle tracking. The suggested model deploys FCA that is configured with rules supporting least sensitive fuzzy “Exclusive or” function as next state logic to handle degrees of uncertainty in rule similarly operations. Indeed, at each step, the update of background in frame difference approaches is determined according to the number of active cells and fuzzy mapping function; so moving vehicles that their gray level is entirely similar to the background gray level are simply detected. Furthermore, an occlusion handling technique based on visual measurement is employed to detect the classes of the vehicle occlusions and segment the vehicle from each occlusive class. The experimental results show that the suggested method is more robust, accurate and powerful than traditional methods, for real-time vehicle detection and tracking.

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References

  1. Chiu C., Ku M., Wang C.: Automatic traffic surveillance system for vision-based vehicle recognition and tracking. J. Inf. Sci. Eng. 26(2), 611–629 (2010)

    Google Scholar 

  2. S. V. Kuntawar, V. D. Chaudhari, and D. A. Patil, Vehicle Identification and Classification: A Survey, Proceedings of the International Conference on Modeling and Simulation in Engineering & Technology (ICMSET), pp. 1–9, India,15–16 Feb. 2014.

  3. Hadi R.A., Sulong G., George L.E.: Vehicle detection and tracking techniques: a concise review. Signal & Image Processing: An International Journal (SIPIJ). 5(1), 1–12 (2014)

    Google Scholar 

  4. Patil S.P., Patil M.B.: Moving vehicle detection: a review. International Journal of Computer Applications. 87(15), 35–37 (2014)

    Article  MathSciNet  Google Scholar 

  5. Y. Long, X. Xiao, X. Shu, and S. Chen, Vehicle Tracking Method using Background Subtraction and Mean Shift Algorithm, Proceedings of the International Conference on E-Product E-Service and E-Entertainment (ICEEE), pp. 1–4, China, 7–9 Nov. 2010.

  6. N. Saunier, and T. Sayed, A Feature-based Tracking Algorithm for Vehicles in Intersections, The 3rd Canadian Conference on Computer and Robot Vision, pp. 1–7, Canada, 7–9 2006.

  7. Chovanec M.: Computer vision vehicle tracking using background subtraction. Journal of Information, Control and Management Systems(JICMS). 1(1), 1–8 (2005)

    Google Scholar 

  8. Ng K.K., Delp E.J.: Background subtraction using a pixel-wise adaptive learning rate for object tracking initialization”, SPIE proceedings. Visual Information Processing and Communication II. 7882, 1–9 (2011)

    Google Scholar 

  9. Płaczek B.: A traffic model based on fuzzy cellular automata. J. Cell. Autom. 8(3), 261–282 (2013)

    MATH  Google Scholar 

  10. M. Shakeri, H. Deldari, H. Foroughi, A. Saberi, and A. Naseri, A Novel Fuzzy Background Subtraction Method Based on Cellular Automata for Urban Traffic Applications, 9th International Conference on Signal Processing (ICSP), pp. 899–902, Beijing, 26–29 2008.

  11. D. R. Nayak, P. K. Patra, and A. Mahapatra, A Survey on Two Dimensional Cellular Automata and Its Application in Image Processing, Proceedings of the International Conference on Emergent Trends in Computing and Communication (ETCC), pp. 1–10, India, 22–23 2014.

  12. Uguz S., Sahin U., Sahin F.: Edge detection with fuzzy cellular automata transition function optimized by PSO. Comput. Electr. Eng. 43, 180–192 (2015)

    Article  Google Scholar 

  13. Maji P., Chaudhuri P.P.: Fuzzy cellular automata for modeling pattern classifier. Transactions on Information and Systems (IEICE). 88(4), 691–702 (2005)

    Article  Google Scholar 

  14. J. P. Carvalho, M. Carola, and J. A. B. Tomé, Using Rule-Based Fuzzy Cognitive Maps to Model Dynamic Cell Behaviour in Voronoi Based Cellular Automata, IEEE International Conference on Fuzzy Systems, pp. 1687–1694, Vancouver, BC, 2006.

  15. Pang C.C.C., Lam W.W.L., Yung N.H.C.: A novel method for resolving vehicle occlusion in a monocular traffic-image sequence. IEEE Trans. Intell. Transp. Syst. 5(issue 3), 129–141 (2004)

    Article  Google Scholar 

  16. M. Chitra, M. K. Geetha, and L. Menaka, Occlusion and Abandoned Object Detection for Surveillance Applications, International Journal of Computer Applications Technology and Research, pp. 708–713, vol. 2, issue 6, 2013.

  17. B. Y. Lee, L. H. Liew, W. S. Cheah, and Y. C. Wang, Occlusion Handling in Videos Object Tracking: A survey, 8th International Symposium of the Digital Earth (ISDE8), pp. 1–6, Malaysia, 26–29 August 2013.

  18. Ku M., Chiu C., Chen H., Hong S.: Visual motorcycle detection and tracking algorithms. WSEAS Trans. Electron. 5(4), 121–131 (2008)

    Google Scholar 

  19. Parekh H.S., Thakore D.G., Jaliya U.K.: A survey on object detection and tracking methods. International Journal of Innovative Research in Computer and Communication Engineering. 2(Issue 2), 2970–2978 (2014)

    Google Scholar 

  20. Sivaraman S., Trivedi M.M.: Looking at vehicles on the road: a survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans. Intell. Transp. Syst. 14(4), 1773–1795 (2013)

    Article  Google Scholar 

  21. Yousaf K., Iftikhar A., Javed A.: Comparative analysis of automatic vehicle classification techniques: a survey. International Journal of Image, Graphics, and Signal Processing (IJIGSP). 4(9), 52–59 (2012)

    Article  Google Scholar 

  22. Musa Z.B., Watada J., Yan S., Ding H.: Dynamic tracking system through PSO and Parzen particle filter. Lect. Notes Comput. Sci. 5712, 220–227 (2009)

    Article  Google Scholar 

  23. Ayanzadeh R., Mousavi A.S., Shahamatnia E.: Fuzzy cellular automata based random numbers generation. Trends in Applied Sciences Research. 7(Issue 1), 96–102 (2012)

    Article  Google Scholar 

  24. Ning J., Yang Y., Zhu F.: Background modeling and fuzzy clustering for motion detection from video. J. Multimed. 8(5), 626–631 (2013)

    Article  Google Scholar 

  25. Y. Chen, B. Wu, and C. Fan, Real-time Vision-based Multiple Vehicle Detection and Tracking for Nighttime Traffic Surveillance, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3352–3358, USA, 11–14 Oct. 2009.

  26. M. Mraz, N. Zimic, I. Lapanja, and I. Bajec, Fuzzy Cellular Automata: From Theory to Applications, Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence( ICTAI ), pp. 320–323, Vancouver, BC, 13–15 Nov 2000.

  27. J. E. Hernandez, and J. Nava, Least Sensitive (Most Robust) Fuzzy “Exclusive or” operations, Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1–6, USA, 18–20 March 2011.

  28. Bedregal B.C., Reiser R.H.S., Dimuro G.P.: Xor-Implications and E-Implications: Classes of Fuzzy Implications Based on Fuzzy Xor. Journal of Electronic Notes in Theoretical Computer Science (ENTCS). 247, 5–18 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  29. Liu Y., Phinn S.R.: Modeling urban development with cellular automata incorporating fuzzy-set approaches. Journal of Computers, Environment and Urban Systems. 27(Issue 6), 637–658 (2003)

    Article  Google Scholar 

  30. Sharma B., Katiyar V.K., Gupta A.K., Singh A.: The automated vehicle detection of highway traffic images by differential morphological profile. Journal of Transportation Technologies (JTTs). 4(2), 150–156 (2014)

    Article  Google Scholar 

  31. Rani S., Kaur B., Bansal D.: Edge detection of MRI images using morphological operators. International Journal of Advanced Research in Computer Science and Software Engineering. 4(issue 4), 1023–1027 (2014)

    Google Scholar 

  32. L. He, Y. Chao, and K. Suzuki, A New Two-Scan Algorithm for Labeling Connected Components in Binary Images, Proceedings of the World Congress on Engineering (WCE), pp. 1–6, London, UK, 4–6 July 2012.

  33. Halabi Y.S.: New algorithm-simulation connected components labeling for binary images. International Journal of Emerging Technology and Advanced Engineering. 3(issue 12), 335–348 (2013)

    Google Scholar 

  34. Kanhere N.K., Birchfield S.T., Sarasua W.A.: Vehicle segmentation and tracking in the presence of occlusions. J. Transp. Res. Board. 1944(Issue 1), 89–97 (2006)

    Article  Google Scholar 

  35. Sigari M.H., Mozayani N., Pourreza H.R.: Fuzzy running average and fuzzy background subtraction: concepts and application. International Journal of Computer Science and Network Security. 8(2), 138–143 (2008)

    Google Scholar 

  36. S. Zehang, G. Bebis, and R. Miller, On-road vehicle detection: A review, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28.5 (2006): 694–711.

  37. K. She, G. Bebis, H. Gu, and R. Miller, Vehicle tracking using on-line fusion of color and shape features, Proceedings The 7th International IEEE Conference on Intelligent Transportation Systems, pp. 731–736, 2004.

  38. X. Shi, H. Ling, E. Blasch, and W. Hu, Context-driven moving vehicle detection in wide area motion imagery, 21st IEEE International Conference on pattern Recognition (ICPR), pp. 2512–2515,2012.

  39. C. Wang, and J. Lien, Automatic vehicle detection using local features—A statistical approach, IEEE Transactions on Intelligent Transportation Systems,9(1):83–96, 2008.

  40. C. H.-Yung, C.-C. Weng and Y.-Y. Chen, Vehicle detection in aerial surveillance using dynamic Bayesian networks, IEEE Transactions on Image Processing, 21.4 (2012): 2152–2159.

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  1. Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby 21526, P.O. Box 832, Alexandria, Egypt

    Saad M. Darwish

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Correspondence to Saad M. Darwish.

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Darwish, S.M. Extension of Cellular Automata for Dynamic Vehicle Tracking. Int. J. ITS Res. 15, 127–140 (2017). https://doi.org/10.1007/s13177-016-0127-x

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  • DOI: https://doi.org/10.1007/s13177-016-0127-x

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