Computer Vision Methods for Automotive Applications
DOI:
https://doi.org/10.21754/tecnia.v30i2.801Keywords:
computer vision, wheel rim detection, vehicle calibration angles, stereo odometryAbstract
Recent advances in computer vision have favored technological developments in the automotive industry. In this work, we present methods relevant to the use of cameras as measurement devices in computer vision and their applications in the automotive industry. The methods are edge and ellipse detection, camera calibration, 3-D reconstruction and stereo vision. The applications include methods for detecting rims in automotive wheels, estimation of the calibration angles of vehicles and the reconstruction of a vehicle's trajectory using stereo vision. The results showed the potential of computer vision methods to solve complex problems in the automotive industry. In conclusion, a set of techniques and applications of computer vision in the automotive industry are presented in order to motivate future developments in this and other related areas.
Downloads
References
[2] J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.
[3] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ: Pearson, 2008.
[4] R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge: Cambridge University Press, 2004.
[5] E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision. Upper Saddle River, NJ: Prentice Hall, 1998.
[6] Y. Ma, S. Soatto, J. Košecká, and S. S. Sastry, An Invitation to 3-D Vision, vol. 26. New York, NY: Springer New York, 2004.
[7] R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,” Commun. ACM, vol. 15, no. 1, pp. 11–15, Jan. 1972.
[8] D. H. Ballard, “Generalizing the Hough transform to detect arbitrary shapes,” Pattern Recognit., vol. 13, no. 2, pp. 111–122, Jan. 1981.
[9] P. R. G. Kurka, J. V. Delgado, C. R. Mingoto, and O. E. R. Rojas, “Automatic estimation of camera parameters from a solid calibration box,” J. Brazilian Soc. Mech. Sci. Eng., vol. 35, no. 2, pp. 93–101, Jun. 2013.
[10] T. Ardeshiri, F. Larsson, F. Gustafsson, T. B. Schön, and M. Felsberg, “Bicycle tracking using ellipse extraction,” in Fusion 2011 - 14th International Conference on Information Fusion, 2011.
[11] Yonghong Xie and Qiang Ji, “A new efficient ellipse detection method,” in Object recognition supported by user interaction for service robots, vol. 2, pp. 957–960.
[12] C. A. Basca, M. Talos, and R. Brad, “Randomized Hough Transform for Ellipse Detection with Result Clustering,” in EUROCON 2005 - The International Conference on “Computer as a Tool,” 2005, pp. 1397–1400.
[13] V. J. Delgado, P. R. Kurka, and E. Cardozo, “Visual odometry in mobile robots,” in IX Latin American Robotics Symposium and IEEE Colombian Conference on Automatic Control, 2011, pp. 1–4.
[14] A. Vedaldi and B. Fulkerson, “Vlfeat: An Open and Portable Library of Computer Vision Algorithms,” in Proceedings of the 18th ACM International Conference on Multimedia, 2010, pp. 1469–1472.
[15] J. V. Delgado and P. R. G. Kurka, “The Use of a Graphic Processing Unit (GPU) in a Real Time Visual Odometry Application,” in 2015 IEEE International Conference on Dependable Systems and Networks Workshops, 2015, pp. 141–146.
[16] D. Scaramuzza and F. Fraundorfer, “Visual Odometry [Tutorial],” IEEE Robot. Autom. Mag., vol. 18, no. 4, pp. 80–92, Dec. 2011.
[17] F. Fraundorfer and D. Scaramuzza, “Visual odometry: Part II: Matching, robustness, optimization, and applications,” IEEE Robot. Autom. Mag., vol. 19, no. 2, pp. 78–90, Jun. 2012.