Abstract
Intelligent Transportation Systems (ITS) helps to improve the quality and quantity of many car traffic parameters. The use of the ITS is possible when the adequate measuring infrastructure is available. Video systems allow for its implementation with relatively low cost due to the possibility of simultaneous video recording of a few lanes of the road at a considerable distance from the camera. The process of tracking can be realized through different algorithms, the most attractive algorithms are Bayesian, because they use the a priori information derived from previous observations or known limitations. Use of this information is crucial for improving the quality of tracking especially for difficult observability conditions, which occur in the video systems under the influence of: smog, fog, rain, snow and poor lighting conditions.
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References
Aczel, A.D.: Complete Business Statistic. Richard D. Irwin (1993)
Bar–Shalom, Y.: Multitarget–Multisensor Tracking: Applications and Advances, vol. II. Artech House, Norwood (1992)
Blackman, S., Poupoli, R.: Modern Tracking Systems. Artech House, Norwoood (1999)
Blackman, S.: Multiple–Target Tracking with Radar Applications. Artech House, Norwood (1986)
Boers, Y., Ehlers, F., Koch, W., Luginbuhl, T., Stone, L.D., Streit, R.L. (eds.): Track Before Detect Algorithm. EURASIP Journal on Advances in Signal Processing, Hindawi (2008)
Brookner, E.: Tracking and Kalman Filtering Made Easy. Willey–Interscience, Hoboken (1998)
Coué, C., Pradalier, C., Laugier, C.: Bayesian Programming Multi–Target Tracking: an Automotive Application. In: Int. Conf. on Field and Service Robotics, Lake Yamanaka, Japan, July 14–16 (2003)
Coué, C., Fraichard, T., Bessiére, P., Mazer, E.: Using Bayesian Programming for Multi–Sensor Multi–Target Tracking in Automotive Applications. In: Int. Conf. on Robotics and Automation, Taipai, Taiwan, May 12–17 (2003)
Gindele, T., Brechtel, S., Schröder, J., Dillmann, R.: Bayesian Occupancy Grid Filter for Dynamic Environments Using Prior Map Knowledge. In: Proceedings of the IEEE Intelligent Vehicles Symposium (2009)
Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering 82 Series D, 35–46 (1960)
Klein, L.A.: Sensor Technologies and Data Requirements for ITS. Artech House ITS library, Norwood (2001)
Stone, L.D., Barlow, C.A., Corwin, T.L.: Bayesian Multiple Target Tracking. Artech House, Norwood (1999)
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Mazurek, P., Okarma, K. (2010). Application of Bayesian a Priori Distributions for Vehicles’ Video Tracking Systems. In: Mikulski, J. (eds) Transport Systems Telematics. TST 2010. Communications in Computer and Information Science, vol 104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16472-9_38
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DOI: https://doi.org/10.1007/978-3-642-16472-9_38
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