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Automatic vehicle recognition in multiple cameras for video surveillance

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Abstract

To efficiently locate identical objects in heterogeneous cameras and possibly propagate reliable information between cameras and refine detection, many techniques were used to recognize vehicles. In this paper, we investigate several key problems and present a novel approach for automatic vehicle recognition (AVR) in multiple cameras for video surveillance application. We propose a level-based region comparison algorithm to AVR in multiple cameras. For improving the recognition accuracy, new license plate recognition method is also proposed. Experimental results show that the proposed algorithm is simple and efficient, and the quality of the composed image can be comparable with the results of the state-of-the-art methods.

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References

  1. Lin, W., Sun, M.-T., Poovendran, R., Zhang, Z.: Group event detection with a varying number of group members for video surveillance. IEEE Trans. Circuits Syst. Video Technol. 20(8), 1057–1067 (2010)

    Article  Google Scholar 

  2. Lin, W., Sun, M.-T., Poovendran, R., Zhang, Z.: Activity recognition using a combination of category components and local models for video surveillance. IEEE Trans. Circuits Syst. Video Technol 18, 1128–1139 (2008)

    Article  Google Scholar 

  3. Duong, T.V., Bui, H.H., Phung, D.Q., Venkatsh, S.: Activity recognition and abnormality detection with the switching hidden semi-Markov model. In: Proceedings of the CVPR 1, 838–845 (2005)

  4. Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I.: Semi-supervised adapted HMMs for unusual event detection. In: Proceedings of the CVPR, 2005

  5. Anagnostopoulos, C.N., Anagnostopoulos, I., Psoroulas, I., Loumos, V., Kayafas, E.: License plate recognition from still images and video sequences: a survey. IEEE Trans. Intell Transp. Syst. 9(3), 377–392 (2008)

    Article  Google Scholar 

  6. Nomura, S.G., Yamanaka, K.J., Katai, O., Kawakami, H.S., Shiose, T.Y.: A novel adaptive morphological approach for degraded character image segmentation. Pattern Recognit. 38(11), 1961–1975 (2005)

    Article  Google Scholar 

  7. Jia, W.J., Zhang, H.F., He, X.J.: Region-based license plate detection. J. Netw. Comput. Appl. 30, 1324–1333 (2007)

    Article  Google Scholar 

  8. Cui, Y.T., Huang, Q.: Extracting characters of license plates from video sequences. Mach. Vis. Appl. 10, 308–320 (1998)

    Article  Google Scholar 

  9. Nomura, S., Yamanaka, K., Katai, O., Kawakami, H., Shiose, T.: A novel adaptive morphological approach for degraded character image segmentation. Pattern Recognit. 38(11), 1961–1975 (Nov. 2005)

    Google Scholar 

  10. Draghici, S.: A neural network based artificial vision system for license late recognition. Int. J. Neural Syst. 8(1), 113–126 (Feb. 1997)

    Google Scholar 

  11. Kim, S.K., Kim, D.W., Kim, H.J.: A recognition of vehicle license plate using genetic algorithm based segmentation. In: Proceedings of the Image Processing 2, 661–664 (1996)

  12. Brugge, M. H. T., Stevens, J. H., Nijhuis, J. A. G., Spaanenburg, L.: License plate recognition using DTCNNs. In: Proceedings of the 5th IEEE international workshop on cellular neural networks and their applications, pp. 212–217 (1998)

  13. Parisi, R., Claudio, E.D.D., Lucarelli, G., Orlandi, G.: Car plate recognition by neural networks and image processing. In: Proceedings of the IEEE International Symposium Circuits and Systems 3, 195–198 (1998)

  14. Yoshimori, S., Mitsukura, Y., Fukumi, M., Akamatsu N.: License plate detection using hereditary threshold determine method. In: Palade V., Howlett, R. J., Jain, L.C., (eds.) vol. 2773, pp. 585–593, Springer, New York (2003)

  15. Kim, K.K., Kim, K.I., Kim J.B., Kim, H. J.: Learning-based approach, for license plate recognition. In: Processing of the IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing 2, 614–623 (2000)

  16. Huang, Y.P., Lai, S.Y., Chuang, W.P.: A template-based model for license plate recognition. In: Proceedings of the IEEE Networking, Sensing and Control, pp. 737–742 (2004)

  17. Comelli, P., Ferragina, P., Granieri, M.N., Stabile, F.: Optical recognition of motor vehicle license plates. IEEE Trans. Veh. Technol. 44(4), 790–799 (1995)

    Article  Google Scholar 

  18. Land, E.H., McCann, J.J.: Lightness and Retinex theory. J. Opt. Soc. Am. 61, 1–11 (1971)

    Article  Google Scholar 

  19. Rao, Y.B., Chen, Z.H., Sun, M-T., Hsu Y-F., Zhang Z.Y.: An effective nighttime video enhancement algorithm, Visual Communications and Image Processing (VCIP), Taiwan, 6–9 Nov. 2011

  20. Gonzalez, R.C., Woods, R.E.: Digital image processing. Person Prentice Hall, New Jersey (2008)

    Google Scholar 

  21. Shi, X.F., Zhao, W.Z., Shen, Y.H.: Automatic license plate recognition system based on color image processing. In: Gervasi O. (ed.) Lecture Notes on Computer Science 3483, Springer, New York, pp. 1159–1168, (2005)

  22. Rao, Y.B., Lin, W.Y., Chen, L.T.: Image-based fusion for video enhancement of nighttime surveillance. Opt. Eng. 49(12), 1–4 (2010)

    Article  Google Scholar 

  23. Abolghasemi, V., Ahmadyfard, A.: A fast algorithm for license plate detection. In: Proceedings of the VISUAL, pp. 468–477 (2007)

  24. Anagnostopoulos, C.N., Anagnostopoulos, I., Loumos, V., Kayafas, E.: A license plate recognition algorithm for intelligent transportation system applications. IEEE Trans. Intell. Transp. Syst. 7(3), 377–392 (2006)

    Article  Google Scholar 

  25. Fan, B., Lin, W., Yang, X.K., An efficient framework for recognizing traffic light in night traffic images, IEEE International Congress on Image and Signal Processing and IEEE International Conference on BioMedical Engineering and Informatics (CISP-BMEI), Chongqing (2012)

  26. Agnes, E.J., Rubem Erichsen Jr, Brunnet L.G.: Model architecture for associative memory in a neural network of spiking neurons. In: Proceedings of Physica A Statistical Mechanics and its Applications, vol. 391, no. 3, pp:843–848 (2012)

  27. Chang, S.L., Chen, L.S., Chung, Y.-C., Chen, S.-W.: Automatic license plate recognition. IEEE Trans. Intell. Transp. Syst. 5(1), 42–53 (2004)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful comments. This work is partly supported by National Science Foundation of China (Grant No.61300092).

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  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan , 610054, China

    Yunbo Rao

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Correspondence to Yunbo Rao.

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Rao, Y. Automatic vehicle recognition in multiple cameras for video surveillance. Vis Comput 31, 271–280 (2015). https://doi.org/10.1007/s00371-013-0917-y

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