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Knowledge-Based Road Traffic Monitoring

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Nature Inspired Problem-Solving Methods in Knowledge Engineering (IWINAC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4528))

Abstract

This article presents a knowledge-based application to study and analyze traffic behavior on major roads, using as the main surveillance artefact a video camera mounted on a relatively high place with a significant image analysis field. The system described presents something new which is the combination of both traditional traffic monitoring systems, that is, monitoring to get information on different traffic parameters and monitoring to detect accidents automatically. Therefore, we present a system in charge of compiling information on different traffic parameters. It also has a surveillance module, which can detect a wide range of the most significant incidents on a freeway or highway.

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References

  1. Cucchiara, R., Piccardi, M., Mello, P.: Image analysis and rule-based reasoning for a traffic monitoringsystem. IEEE Transactions on Intelligent Transportation Systems 1(2), 119–130 (2000)

    Article  Google Scholar 

  2. Fernández, M.A., Fernández-Caballero, A., López, M.T., Mira, J.: Length-Speed Ratio (LSR) as a characteristic for moving elements real-time classification. Real-Time Imaging 9(1), 49–59 (2003)

    Article  Google Scholar 

  3. Fernández-Caballero, A., Mira, J., Fernández, M.A., Lopez, M.T.: Segmentation from motion of non-rigid objects by neuronal lateral interaction. Pattern Recognition Letters 22(14), 1517–1524 (2001)

    Article  MATH  Google Scholar 

  4. Fernández-Caballero, A., Mira, J., Delgado, A.E., Fernández, M.A.: Lateral interaction in accumulative computation: A model for motion detection. Neurocomputing 50, 341–364 (2003)

    Article  MATH  Google Scholar 

  5. Fernández-Caballero, A., Fernández, M.A., Mira, J., Delgado, A.E.: Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation. Pattern Recognition 36(5), 1131–1142 (2003)

    Article  MATH  Google Scholar 

  6. Gupte, S., Masoud, O., Martin, R.F.K., Papanikiolopoulos, N.P.: Detection, and classification of vehicles. IEEE Transactions on Intelligent Transportation Systems 3(1), 37–47 (2002)

    Article  Google Scholar 

  7. Ha, D.M., Lee, J.M., Kim, Y.D.: Neural-edge-based vehicle detection and traffic parameter extraction. Image and Vision Computing 22(11), 899–907 (2004)

    Article  Google Scholar 

  8. Hsieh, J.W., Yu, S.H., Chen, Y.S., Hu, W.F.: Automatic traffic surveillance system for vehicle tracking and classification. IEEE Transactions on Intelligent Transportation Systems 7(2), 175–187 (2006)

    Article  Google Scholar 

  9. Ji, X., Wei, Z., Feng, Y.: Effective vehicle detection technique for traffic surveillance systems. Journal of Visual Communication and Image Representation 17(3), 647–658 (2006)

    Article  Google Scholar 

  10. Kastrinaki, V., Zervakis, M., Kalaitzakis, K.: A survey of video processing techniques for traffic applications. Image and Vision Computing 21(4), 359–381 (2003)

    Article  Google Scholar 

  11. López, M.T., Fernández-Caballero, A., Fernández, M.A., Mira, J., Delgado, A.E.: Visual surveillance by dynamic visual attention method. Pattern Recognition 39(11), 2194–2211 (2006)

    Article  Google Scholar 

  12. Mira, J., Delgado, A.E., Fernández-Caballero, A., Fernández, M.A.: Knowledge modelling for the motion detection task: The algorithmic lateral inhibition method. Expert Systems with Applications 27(2), 169–185 (2004)

    Article  Google Scholar 

  13. Rad, R., Jamzad, M.: Real time classification and tracking of multiple vehicles in highways. Pattern Recognition Letters 26(10), 1597–1607 (2005)

    Article  Google Scholar 

  14. Tai, J.C., Tseng, S.T., Lin, C.P., Song, K.T.: Real-time image tracking for automatic traffic monitoring and enforcement applications. Image and Vision Computing 22(6), 485–501 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. Instituto de Investigación en Informática de Albacete (I3A) and, Escuela Politécnica Superior de Albacete, Universidad de Castilla-La Mancha, 02071-Albacete, España

    Antonio Fernández-Caballero, Francisco J. Gómez & Juan López-López

Authors
  1. Antonio Fernández-Caballero
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  2. Francisco J. Gómez
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  3. Juan López-López
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Editor information

José Mira José R. Álvarez

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© 2007 Springer Berlin Heidelberg

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Fernández-Caballero, A., Gómez, F.J., López-López, J. (2007). Knowledge-Based Road Traffic Monitoring. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_20

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  • DOI: https://doi.org/10.1007/978-3-540-73055-2_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73054-5

  • Online ISBN: 978-3-540-73055-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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