Abstract
In this research, we addressDiamantas, Sotirios Ch. Dasgupta, Prithviraj an important problem in mobile robotics - how to estimate the speed of a moving robot or vehicle using optical flow obtained from a series of images of the moving robot captured by a camera. Our method generalizes several restrictions and assumptions that have been used previously to solve this problem - we use an uncalibrated camera, we do not use any reference points on the ground or on the image, and we do not make any assumptions on the height of the moving target. The only known parameter is the camera distance from the ground. In our method we exploit the optical flow patterns generated by varying the camera focal length in order to pinpoint the principal point on the image plane and project the camera height on the image plane. This height is then used to estimate the speed of the target.
This research has been supported by the U.S. Office of Naval Research grant no. N000140911174 as part of the COMRADES project.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The non-parametric method we have developed for outlier removal aims at avoiding the known risks entailing the use of parametric methods, such as RANSAC.
References
Indu, S., Gupta, M., Bhattacharyya, A.: Vehicle tracking and speed estimation using optical flow method. Int. J. Eng. Sci. Technol. 3(1), 429–434 (2011)
Dailey, D.J., Cathey, F.W., Pumrin, S.: An algorithm to estimate mean traffic speed using uncalibrated cameras. IEEE Trans. Intell. Transp. Syst. 1(2), 98–107 (2000)
Dogan, S., Temiz, M.S., Kulur, S.: Real time speed estimation of moving vehicles from side view images from an uncalibrated video camera. Sensors 10(5), 4805–4824 (2010)
Honegger, D., Greisen, P., Meier, L., Tanskanen, P., Pollefeys, M.: Real-time velocity estimation based on optical flow and disparity matching. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5177–5182 (2012)
Grammatikopoulos, L., Karras, G., Petsa, E.: Automatic estimation of vehicle speed from uncalibrated video sequences. In: Proceedings of the International Symposium on Modern Technologies, Education and Professional Practice in Geodesy and Related Fields, pp. 332–338 (2005)
Muller, J., Paul, O., Burgard, W.: Probabilistic velocity estimation for autonomous miniature airships using thermal air flow sensors. In: Proceedings of the International Conference on Robotics and Automation, pp. 39–44 (2012)
Bauer, D., Belbachir, A.N., Donath, N., Gritsch, G., Kohn, B., Litzenberger, M., Posch, C., Schon, P., Schraml, S.: Embedded vehicle speed estimation system using an asynchronous temporal contrast vision sensor. EURASIP J. Embed. Syst. 2007(1), 1–12 (2007)
Kassem, N., Kosba, A.E., Youssef, M.: RF-based vehicle detection and speed estimation. In: Proceedings of the 75th IEEE Vehicular Technology Conference, pp. 1–5 (2012)
Ernst, J.M., Ndoye, M., Krogmeier, J.V., Bullock, D.M.: Maximum-likelihood speed estimation using vehicle-induced magnetic signatures. In: IEEE International Conference on Intelligent Transportation Systems, pp. 1–6 (2009)
Chen, Z., Pears, N., Liang, B.: A method of visual metrology from uncalibrated images. Pattern Recogn. Lett. 27(13), 1447–1456 (2006)
Criminisi, A., Reid, I., Zisserman, A.: Single view metrology. Int. J. Comput. Vis. 40(2), 123–148 (2000)
Momeni-K., M., Diamantas, S.C., Ruggiero, F., Siciliano, B.: Height estimation from a single camera view. In: Proceedings of the International Conference on Computer Vision Theory and Applications, pp. 358–364. SciTePress (2012)
Fayman, J.A., Sudarsky, O., Rivlin, E., Rudzsky, M.: Zoom tracking and its applications. Mach. Vis. Appl. 13(1), 25–37 (2001)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Kong H., Audibert, J.-Y., Ponce, J.: Vanishing point detection for road detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 96–103 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Diamantas, S.C., Dasgupta, P. (2014). Active Vision Speed Estimation from Optical Flow. In: Natraj, A., Cameron, S., Melhuish, C., Witkowski, M. (eds) Towards Autonomous Robotic Systems. TAROS 2013. Lecture Notes in Computer Science(), vol 8069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43645-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-662-43645-5_18
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-43644-8
Online ISBN: 978-3-662-43645-5
eBook Packages: Computer ScienceComputer Science (R0)