Augmented Reality Aspects of Object Recognition in Driver Support Systems
Zsombor Paróczi*, István Nagy**,
Csaba Gáspár-Papanek**, Zsolt T. Kardkovács*,
Endre Varga*, Ádám Siegler***, and Péter Lucz***
*U1 Research Ltd., 2 Gábor Dénes str., INFOPARK, H-1117 Budapest, Hungary
**Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, 2 Magyar tudosok blvd., H-1117 Budapest, Hungary
***Top-Map Plc., 105-113 Bartók Béla str., H-1115 Budapest, Hungary
-  R. T. Azuma, “A Survey of Augmented Reality,” Presence – Teleoperators and Virtual Environments, Vol.6, pp. 355-385, 1997.
-  P. Baranyi and A. Csapo, “Cognitive infocommunications: CogInfoCom,” In Proc. IEEE 11th Int. Symposium on Computational Intelligence and Informatics (CINTI’2010), pp. 141-146, Budapest, Hungary, November 2010.
-  “1st International Workshop on Cognitive Infocommunications (CogInfoCom 2010),” 2010.
-  T. Lang, B. MacIntyre, and I. J. Zugaza, “Massively Multiplayer Online Worlds as a Platform for Augmented Reality Experiences,” In IEEE Virtual Reality 2008, 2008.
-  A. Phillips, “Games in AR: Types and Technologies,” In 8th IEEE Int. Symposium on Mixed and Augmented Reality (ISMAR 2009), Orlando, USA, October 2009.
-  M. Lalonde and Y. Li, “Road Sign Recognition – Survey of the State of the Art,” Technical Report CRIM-IIT-95/09-35, Centre de recherche informatique du Montreal, 1995.
-  S. Estable, J. Schick, F. Stein, R. Janssen, R. Ott,W. Ritter, and Y.-J. Zheng, “A real-time traffic sign recognition system,” In Proc. IEEE Intelligent Vehicles ’94 Symposium, pp. 213-218, 1994.
-  V. Rehrmann, R. Lakmann, and L. Priese, “A Parallel System for Realtime Traffic Sign Recognition,” In Int. Workshop on Advanced Parallel Processing Technologies ’95 (APPT), pp. 72-78, 1995.
-  C. Bahlmann, Y. Zhu, V. Ramesh, M. Pellkofer, and T. Koehler, “A system for traffic sign detection, tracking, and recognition using color, shape, and motion information,” In Proc. IEEE Intelligent Vehicles 2005 Symposium, pp. 255-260, 2005.
-  H. Ishida, T. Takahashi, I. Ide, Y. Mekada, and H. Murase, “Identification of degraded traffic sign symbols by a generative learning method,” In Proc. 18th Int. Conf. Pattern Recognition (ICPR 2006), Vol.1, pp. 531-534, 2006.
-  P. Siegmann, R. J. López-Sastre, P. Gil-Jiménez, S. Lafuente-Arroyo, and S.Maldonado-Bascón, “Fundaments in Luminance and Retroreflectivity Measurements of Vertical Traffic Signs Using a Color Digital Camera,” IEEE Trans. on Instrumentation and Measurement, Vol.57, No.3, pp. 607-615, 2008.
-  A. Broggi, P. Cerri, P.Medici, P. P. Porta, and G. Ghisio, “Real Time Road Signs Recognition,” In Proc. IEEE Intelligent Vehicles 2007 Symposium, pp. 981-986, 2007.
-  A. de la Escalera, J. M. Armignol, and M. Mata, “Traffic Sign Recognition and Analysis for Intelligent Vehicles,” Image and Vision Computing, Vol.21, No.3, pp. 247-258, 2003.
-  X. Gao, K. Hong, P. Passmore, L. Podladchikova, and D. Shaposhnikov, “Colour Vision Model-Based Approach for Segmentation of Traffic Signs,” EURASIP J. on Image and Video Processing, 2008, pp. 1-7, 2008.
-  U. L. Jau, C. S. Teh, and G.W. Ng, “A comparison of RGB and HSI color segmentation in real-time video images: A preliminary study on road sign detection,” In Proc. Int. Symp. Information Technology ITSim 2008, Vol.4, pp. 1-6, 2008.
-  Y. Aoyagi and T. Asakura, “A study on traffic sign recognition in scene image using genetic algorithms and neural networks,” In Proc. IEEE IECON 22nd Int. Industrial Electronics, Control, and Instrumentation Conf., Vol.3, pp. 1838-1843, 1996.
-  T.Warsop and S. Singh, “Distance-invariant sign detection in highdefinition video,” In Proc. IEEE 9th Int. Cybernetic Intelligent Systems (CIS) Conference, pp. 1-6, 2010.
-  G. Piccioli, E. de Micheli, P. Parodi, and M. Campani, “Robust method for road sign detection and recognition,” Image and Vision Computing, Vol.14, No.3, pp. 209-223, 1996.
-  P. Viola and M. J. Jones, “Robust Real-time Face Detection,” Int. J. of Computer Vision, Vol.57, No.2, pp. 137-154, 2004.
-  D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. of Computer Vision, Vol.60, No.2, pp. 91-110, 2004.
-  H. Bay, T. Tuytelaars, and L. v. Gool, “SURF: Speeded Up Robust Features,” Computer Vision and Image Understanding, Vol.110, No.3, pp. 346-359, 2008.
-  X. Baro, S. Escalera, J. Vitria, O. Pujol, and P. Radeva, “Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification,” IEEE Trans. on Intelligent Transportation Systems, Vol.10, No.1, pp. 113-126, 2009.
-  A. P. Karla Brkic and S. Segvic, “Traffic sign detection as a component of an automated traffic infrastructure inventory system,” 33rd annual Workshop of the Austrian Association for Pattern Recognition, 2009.
-  Y. Gu, T. Yendo, M. P. Tehrani, T. Fujii, and M. Tanimoto, “A new vision system for traffic sign recognition,” In Proc. IEEE Intelligent Vehicles Symp. (IV), pp. 7-12, 2010.
-  J. Schlosser, M. Montemerlo, and K. Salisbury, “Intelligent road sign detection using 3D scene geometry,” In Proc. IEEE/RSJ Int Intelligent Robots and Systems (IROS) Conf., pp. 740-745, 2010.
-  R. Timofte, K. Zimmermann, and L. V. Gool, “Multi-view traffic sign detection, recognition, and 3D localisation,” In Proc.Workshop Applications of Computer Vision (WACV), pp. 1-8, 2009.
-  D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf, “Survey of Pedestrian Detection for Advanced Driver Assistance Systems,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.32, No.7, pp. 1239-1258, Jul. 2010.
-  S. Munder and D. M. Gavrila, “An Experimental Study on Pedestrian Classification,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.28, No.11, pp. 1863-1868, 2006.
-  “Nature-inspired Smart Information Systems Project.”
-  C. Gáspár-Papanek, Z. T. Kardkovács, G. Szabó, E. Sipocz, G. Pécsi, and M. Szoke, “Image Pattern Recognition by Ensemble of Classifiers,” Competition Riport, 2007.
-  “Cross Industry Standard Process for Data Mining.”
-  T. Lindeberg, “Ensemble Methods in Machine Learning,” IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 465-470, 1996.
-  H. Fujiyoshi and A. J. Lipton, “Real-Time Human Motion Analysis by Image Skeletonization,” IEICE Trns. on Information and Systems E Seried D, Vol.87, No.1, pp. 113-120, 2004.
-  G. Shakhnarovich, T. Darrell, and P. Indyk, “Nearest-Neighbor Methods in Learning and Vision,” The MIT Press, 2006.
-  G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, Vol.70, pp. 489-501, 2006.
-  T. G. Dietterich, “Ensemble Methods in Machine Learning,” Lectures Notes in Computer Science, Vol.1857, pp. 1-15, 2000.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.