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Recognition of occluded traffic signs based on two-dimensional linear discriminant analysis

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EN
Abstrakty
EN
Traffic signs recognition involving digital image analysis is getting more and more popular. The main problem associated with visual recognition of traffic signs is associated with difficult conditions of image acquisition. In the paper we present a solution to the problem of signs occlusion. Presented method belongs to the group of appearance-based approaches, employing template matching working in the reduced feature space obtained by Linear Discriminant Analysis. The method deals with all types of signs, regarding their shape and color in contrast to commercial systems, installed in higher-class cars, that only detect the round speed limit signs and overtaking restrictions. Finally, we present some experiments performed on a benchmark databases with different kinds of occlusion.
Rocznik
Strony
10--13
Opis fizyczny
Bibliogr. 17 poz.
Twórcy
  • West Pomeranian University of Technology, Dept. of Computer Science and Information Systems, Żołnierska 52 , 71-210 Szczecin, Poland
Bibliografia
  • [1] Eichner , M. L., Breckon , T.P.: Integrated Speed Limit Detection and Recognition from Real-Time Video, In Proc. IEEE Intelligent Vehicles Symposium, IEEE, pp. 626-631 (2008)
  • [2] Bahlmann, C., Ying Zhu, Ramesh , V., Pellkofer , M., Koehler , T.: A system for traffic sign detection, tracking, and recognition using color, shape, and motion information, Intelligent Vehicles Symposium, 2005. Proceedings. IEEE , vol., no., pp.255-260 (2005)
  • [3] Siegmann, P., Lafuente-Arroyo , S., Maldonado - Bascon , S., Gil-Jimenez, P., Goomez -Moreno , H.: Automatic evaluation of traffic sign visibility using SVM recognition methods, Proceedings of the 5th WSEAS International Conference on Signal Processing, Computational Geometry & Arti cial Vision, ISCGAV’05, pp. 170-175 (2005)
  • [4] Thanh Bui-Minh, Ghita , O., Whelan , P.F., Trang Hoang , Vinh Quang Truong : Two algorithms for detection of mutually occluding traffic signs, Control, Automation and Information Sciences (ICCAIS), 2012 International Conference on , pp. 120-125 (2012)
  • [5] Kard kovacs , Z.T., Paroczi , Z., Varga , E., Siegler, A., Lucz, P.: Real-time traffic sign recognition system, Cognitive Infocommunications (CogInfoCom), 2011 2nd International Conference on , vol., no., pp.1-5 (2011)
  • [6] Hazelho , L., Ivo Creusen , I., van de Wouw , D., de With P.H.N., Large-scale classiffcation of traffic signs under real-world conditions, Proc. SPIE 8304, Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI (2012)
  • [7] Kus, M.C., Gokmen, M., Etaner -Uyar , S.: Traffic sign recognition using Scale Invariant Feature Transform and color classification, Computer and Information Sciences, 2008. ISCIS’08. 23rd International Symposium on, pp.1-6 (2008)
  • [8] Escalera S., Baro X., Pujol O., Vitria J., Radeva P.: Traffic-Sign Recognition Systems, SpringerBriefs in Computer Science, Springer London (2011)
  • [9] Mogelmose , A., Trivedi , M.M., Moeslund , T.B.: Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey, Intelligent Transportation Systems, IEEE Transactions on , vol.13, no.4, pp.1484-1497 (2012)
  • [10] Zakir U., Zafar I.,Edirisinghe A. E.: Road Sign Detection and Recognition by using Local Energy Based Shape Histogram (LESH), International Journal of Image Processing, Vol. 4 Iss. 6, pp. 566-582 (2011)
  • [11] Ihara A., Fujiyoshi H., Takagi M., Kumon H., Tamatsu Y.: Improved Matching Accuracy in Traffic Sign Recognition by Using Different Feature Subspaces, Machine Vision Applications 2009 (MVA2009), 3-26, pp. 130-133 (2009)
  • [12] Yang H., Liu C., Liu K., Huang S.: Traffic Sign Recognition in disturbing Environments, Proc. of the 14 th Intl. Symp. on Methodologies for Intelligent Systems, pp. 28-31 (2003)
  • [13] Park J-G., Kim K-J.: A method for feature extraction of traffic sign detection and the system for real world scene, Emerging Signal Processing Applications (ESPA), 2012 IEEE International Conference on , pp.13-16 (2012)
  • [14] Fleyeh , H., Davami , E.: Eigen-based traffic sign recognition, Intelligent Transport Systems, IET , vol.5, no.3, pp.190-196 (2011)
  • [15] Johansson , G., Rumar , K.: Drivers and road signs: A preliminary investigation of the capacity of car drivers to get information from road signs. Ergonomics, Vol 9(1), 57-62, (1966)
  • [16] Kukharev G., Forczmans ki P.: Two-Dimensional LDA Approach to Image Compression and Recognition, Computing, Multimedia and Intelligent Techniques, vol.2, no. 1, 87-98 (2006)
  • [17] Houben S., Stall kamp J., Salmen J., Schlipsing M., Igel C.: Detection of Traffic Signs in Real-World Images: The German Traffic Sign Detection Benchmark, International Joint Conference on Neural Networks (2013)
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Bibliografia
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