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A new method to estimate dimensions of vehicle using a single camera

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Języki publikacji
EN
Abstrakty
EN
The capability of a telematic vision system of estimating dimensions of vehicles is used for such tasks as vehicle classification or preselection of vehicles that violate local vehicle size limitations. Also in some European countries dimensions of heavy vehicles must obey some global regulations. Furthermore, vehicle size estimation allows us to determine the structure of traffic and can be very useful for advanced traffic flow control. Many existing Intelligent Transportation Systems consist of a large number of video cameras located in various places e.g., the ITS in Wrocław uses more than 1400 cameras. In this paper we propose a new method developed by the ArsNumerica Group and CyberTech Scientific Circle for the precise estimation of vehicle sizes using a single camera. The method does not require the entering of measurements such as the distance between lane lines or the height of the camera above the roadway. Only one vehicle’s dimensions are used for calibration. The proposed method is easy to implement and may be applied with the OpenCV library which is free both for academic and commercial use. The method is tested on real-world video streams. The obtained results are shown and analyzed in the paper.
Rocznik
Strony
3--7
Opis fizyczny
Bibliogr. 15 poz.
Twórcy
autor
  • Wrocław University of Science and Technology, Faculty of Electronics, ul. Janiszewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Science and Technology, Faculty of Electronics, ul. Janiszewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Science and Technology, Faculty of Electronics, ul. Janiszewskiego 11/17, 50-372 Wrocław, Poland
  • Wrocław University of Science and Technology, Faculty of Electronics, ul. Janiszewskiego 11/17, 50-372 Wrocław, Poland
autor
  • Wrocław University of Science and Technology, Faculty of Electronics, ul. Janiszewskiego 11/17, 50-372 Wrocław, Poland
Bibliografia
  • [1] BUCH, N., VELASTIN, S.A., ORWELL, J.: A Review of Computer Vision Techniques for the Analysis of Urban Traffic, IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 3, Sept. 2011
  • [2] ZHANG, J., et al.: Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, vol. 12 no.4, pp. 1624-1639.
  • [3] BAZAN, M., et al.: Detection of vehicles moving in the wrong direction, Archives of Transport System Telematics. 2016, vol. 9, no 1, pp. 3-9.
  • [4] BAZAN, M., et al.: Green wave optimisation, Archives of Transport System Telematics. 2016, vol. 9, no. 3, pp. 3-8.
  • [5] LAMMER, S., HELBING, D.: Self-control of traffic lights and vehicle flows in urban road networks. Journal of Statistical Mechanics: Theory and Experiment 2008.04 (2008): P04019.
  • [6] BAZAN, M. et al.: Road traffic predictions across major city intersections using multilayer perceptrons and data from multiple intersections located in various places, IET Intelligent Transport Systems. 2016, vol. 10, no 7, pp. 469-475
  • [7] BAZAN, M., et al.: Estimation of travel time in the city based on intelligent transportation system traffic data with the use of neural networks, Dependability Engineering and Complex Systems : proceedings of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, Springer.
  • [8] KAEHLER, A., BRADSKI, G.: Learning OpenCV 3 Computer Vision in C++ with the OpenCV Library, O’Reilly Media, 2016
  • [9] [On-line] OpenCV Documentation Available: http://opencv.org/documentation.html [date of access: 20.01.2016]
  • [10] MASOUD, O., PAPANIKOLOPOULOS, N.: Using geometric primitives to calibrate traffic scenes, in Proc. IEEE/RSJ Intl. Conf. Intelligent Robots and Systems, vol. 2, 2004, pp. 1878-1883.
  • [11] FEI-YUE, W.: A simple and analytical procedure for calibrating extrinsic camera parameters. IEEE Transactions on Robotics and Automation 20.1 (2004): 121-124.
  • [12] DUBSKA, M., HEROUT, A., SOCHOR, J.: Automatic Camera Calibration for Traffic Understanding. BMVC. 2014
  • [13] NEERAJ, K., BIRCHFIELD, S., SARASUA, W.: Automatic camera calibration using pattern detection for vision-based speed sensing.” Transportation Research Record: Journal of the Transportation Research Board 2086 (2008): 30-39.
  • [14] KUNFENG, W., et al. Research on lane-marking line based camera calibration. Vehicular Electronics and Safety, 2007. ICVES. IEEE International Conference on. IEEE, 2007.
  • [15] WANG, J.M., et al.: Shadow detection and removal for traffic images. In Networking, Sensing and Control, 2004 IEEE International Conference on (Vol. 1, pp. 649-654). IEEE.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f76a70aa-9504-4dc4-85e8-95ab56bac8c8
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