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Pose Estimation for Vehicles Based on Binocular Stereo Vision in Urban Traffic

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

Extensive research has been carried out in the field of driver assistance systems in order to increase road safety and comfort. We propose a pose estimation algorithm based on binocular stereo vision for calculating the pose of on-road vehicles and providing reference for the decision of driving assistant system, which is useful for behavior prediction for vehicles and collision avoidance. Our algorithm is divided into three major stages. In the first part, the vehicle is detected and roughly located on the disparity map. In the second part, feature points on the vehicle are extracted by means of license plate detection algorithm. Finally, pose information including distance, direction and its variation is estimated. Experimental results prove the feasibility of the algorithm in complex traffic scenarios.

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References

  1. Bertolazzi, E., Biral, F., Da Lio, M., et al.: Supporting drivers in keeping safe speed and safe distance: the saspence subproject within the European framework programme 6 integrating project Prevent. IEEE Trans. Int. Transp. Syst. 11(3), 525–538 (2010)

    Article  Google Scholar 

  2. Skutek, M., Mekhaiel, M., Wanielik, G.: A Precrash system based on radar for automotive applications. In: Proceedings of IEEE of Intelligent Vehicles Symposium, Columbus, pp. 37–41 (2003)

    Google Scholar 

  3. Velupillai, S., Guvenc, L.: Laser scanners for driver-assistance systems in intelligent vehicles. IEEE Control Syst. Mag. 29(2), 17–19 (2009)

    Article  Google Scholar 

  4. Alonso, L., Pérez-Oria, J., Fernández, M., Rodríguez, C., Arce, J., Ibarra, M., Ordoñez, V.: Genetically tuned controller of an adaptive cruise control for urban traffic based on ultrasounds. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part II. LNCS, vol. 6353, pp. 479–485. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Nedevschi, S., Danescu, R., Frentiu, D., et al.: High accuracy stereovision approach for obstacle detection on non-planar roads. In: Proceedings of IEEE Intelligent Engineering Systems (INES), Cluj Napoca, Romania, pp. 211–216 (2004)

    Google Scholar 

  6. Broggi, A., Bertozzi, M., Fascioli, A., Guarino, C., Piazzi, A.: Visual perception of obstacles and vehicles for platooning. IEEE Trans. Intell. Transp. Syst. 1(3), 164–176 (2000)

    Article  Google Scholar 

  7. Sun, Z., Bebis, G., Miller, R.: On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 694–711 (2006)

    Article  Google Scholar 

  8. Michels, J., Saxena, A., Ng, A.Y.: High speed obstacle avoidance using monocular vision and reinforcement learning. In: 22nd International Conference on Machine Learning, Bonn, Germany, pp. 593–600 (2005)

    Google Scholar 

  9. Gat, I., Benady, M., Shashua, A.: A monocular vision advance warning system for the automotive aftermarket. SAE Technical Paper (2005)

    Google Scholar 

  10. Yanamura, Y., Goto, M., Nishiyama, D., Soga, M., Nakatani, H., Saji, H.: Extraction and tracking of the license plate using Hough transform and voted block matching. In: Proceedings of IEEE Intelligent Vehicles Symposium, pp. 243–246 (2003)

    Google Scholar 

  11. Chengwen, H., Yannan, Z., Jiaxin, W., Zehong, Y.: An improved method for the character recognition based on SVM. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, pp. 457–461 (2006)

    Google Scholar 

  12. Hung, K.M., Hsieh, C.T.: A real-time mobile vehicle license plate detection and recognition. J. Sci. Eng. 13, 433–442 (2010)

    Google Scholar 

  13. McGlove, C., Mikhail, E., Bethel, J.: Manual of Photogrametry. American Society For Photogrammetry and Remote Sensing, New York (2004)

    MATH  Google Scholar 

  14. Hartley, R., Zisserman, A.: Multiple View Geometry In Computer Vision. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  15. Yang, H., Wang, F., Chen, L., He, Y., He, Y.: Robust pose estimation algorithm for approximate coplanar targets. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS, vol. 8589, pp. 350–361. Springer, Heidelberg (2014)

    Google Scholar 

  16. Kumagai, T., Sakaguchi, Y., Okuwa, M. et al.: Prediction of driving behavior through probabilistic inference. In: Proceedings of 8th International. Conference on Engineering Applications of Neural Networks, pp. 117–123 (2003)

    Google Scholar 

  17. Hsiao, T.: Time-varying system identification via maximum a posteriori estimation and its application to driver steering models. In: American Control Conference, pp. 684–689. IEEE (2008)

    Google Scholar 

  18. Hu, Z., Uchimura, K.: UV-disparity: an efficient algorithm for stereovision based scene analysis. In: Proceedings of IEEE on Intelligent Vehicles Symposium, pp. 48–54. IEEE (2005)

    Google Scholar 

  19. Song, Y., Wang, F., Gao, S., Yang, H., He, Y.: Error tracing and analysis of vision measurement system. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2014. LNCS, vol. 8588, pp. 729–740. Springer, Heidelberg (2014)

    Google Scholar 

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Acknowledgment

This work is sponsored by National Natural Science Foundation of China (No.612173366) and National High Technology Research and Development Program of China (No.2013AA014601).

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Correspondence to Fei Wang .

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Liu, P., Wang, F., He, Y., Dong, H., Yang, H., Yang, Y. (2015). Pose Estimation for Vehicles Based on Binocular Stereo Vision in Urban Traffic. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_44

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

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