Learning a Confidence Measure for Real-Time Egomotion Estimation

  • Stephanie LessmannEmail author
  • Jens Westerhoff
  • Mirko Meuter
  • Josef Pauli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)


This paper presents a method to generate a meaningful confidence measurement during online real-time egomotion estimation of a vehicle using a monocular camera. This confidence measurement should give the information whether the signal fulfills a certain accuracy range in all parameters or not. For that reason features from an optical flow field incorporating the egomotion error are determined and a confidence measurement is learned using ground truth egomotion data that we obtain from an offline bundle adjustment before. We show that our confidence measurement gives reliable results and can further be used to filter the egomotion estimation using a Kalman filter. Incorporating the knowledge of the egomotion accuracy determined by the confidence we are able to update the confidence measure for the filtered results. This leads to an improved system availability.


Kalman Filter Optical Flow Support Vector Regression System Availability Confidence Measurement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Agrawal, A., Chellappa, R.: Robust ego-motion estimation and 3-D model refinement using surface parallax. IEEE Trans. Image Process. 15(5), 1215–1225 (2006)CrossRefGoogle Scholar
  2. 2.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)Google Scholar
  3. 3.
    Bouguet, J.Y.: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corporation 5, 1–10 (2001)Google Scholar
  4. 4.
    Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: 20th International Conference on Pattern Recognition (ICPR), pp. 3121–3124. IEEE (2010)Google Scholar
  5. 5.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  6. 6.
    Chen, Y.W., Lin, C.J.: Combining SVMs with various feature selection strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds.) Feature Extraction, pp. 315–324. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  8. 8.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Goecke, R., Asthana, A., Pettersson, N., Petersson, L.: Visual vehicle egomotion estimation using the fourier-mellin transform. In: 2007 IEEE Intelligent Vehicles Symposium, pp. 450–455. IEEE (2007)Google Scholar
  11. 11.
    Guo, G., Fu, Y., Dyer, C.R., Huang, T.S.: Head pose estimation: classification or regression?. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)Google Scholar
  12. 12.
    Haeusler, R., Nair, R., Kondermann, D.: Ensemble learning for confidence measures in stereo vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 305–312 (2013)Google Scholar
  13. 13.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN: 0521540518Google Scholar
  14. 14.
    Huber, P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35(1), 73–101 (1964)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Jaegle, A., Phillips, S., Daniilidis, K.: Fast, robust, continuous monocular egomotion computation. arXiv preprint (2016). arXiv:1602.04886
  16. 16.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82(Series D), 35–45 (1960)CrossRefGoogle Scholar
  17. 17.
    Kondermann, C., Kondermann, D., Jähne, B., Garbe, C.S.: An adaptive confidence measure for optical flows based on linear subspace projections. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 132–141. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Lessmann, S., Siegemund, J., Meuter, M., Westerhoff, J., Pauli, J.: Improving robustness for real-time vehicle egomotion estimation. In: Intelligent Vehicles Symposium (2016)Google Scholar
  19. 19.
    Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2(2), 164–168 (1944). JSTORMathSciNetzbMATHGoogle Scholar
  20. 20.
    Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Motten, A., Claesen, L., Pan, Y.: Binary confidence evaluation for a stereo vision based depth field processor SoC. In: 2011 First Asian Conference on Pattern Recognition (ACPR), pp. 456–460. IEEE (2011)Google Scholar
  22. 22.
    Musleh, B., Martin, D., de la Escalera, A., Guinea, D.M., Garcia-Alegre, M.C.: Estimation and prediction of the vehicle’s motion based on visual odometry and Kalman filter. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds.) ACIVS 2012. LNCS, vol. 7517, pp. 491–502. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  23. 23.
    Neufeld, A., Berger, J., Becker, F., Lenzen, F., Schnörr, C.: Estimating vehicle ego-motion and piecewise planar scene structure from optical flow in a continuous framework. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 41–52. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24947-6_4 CrossRefGoogle Scholar
  24. 24.
    Pink, O., Moosmann, F., Bachmann, A.: Visual features for vehicle localization and ego-motion estimation. In: 2009 IEEE Intelligent Vehicles Symposium, pp. 254–260. IEEE (2009)Google Scholar
  25. 25.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. In: ACM transactions on graphics (TOG), vol. 25, pp. 835–846. ACM (2006)Google Scholar
  26. 26.
    Society of Automotive Engineers. Vehicle Dynamics Committee: Vehicle Dynamics Terminology: SAE J670e: Report of Vehicle Dynamics Committee Approved July 1952 and Last Revised July 1976. Handbook supplement, Society of Automotive Engineers (1978)Google Scholar
  27. 27.
    Stein, G.P., Mano, O., Shashua, A.: A robust method for computing vehicle ego-motion. In: Proceedings of the IEEE Intelligent Vehicles Symposium, IV 2000, pp. 362–368. IEEE (2000)Google Scholar
  28. 28.
    Torr, P.H., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78(1), 138–156 (2000)CrossRefGoogle Scholar
  29. 29.
    Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)zbMATHGoogle Scholar
  30. 30.
    Weydert, M.: Model-based ego-motion and vehicle parameter estimation using visual odometry. In: 2012 16th IEEE Mediterranean Electrotechnical Conference (MELECON), pp. 914–919. IEEE (2012)Google Scholar
  31. 31.
    Yamaguchi, K., Kato, T., Ninomiya, Y.: Vehicle ego-motion estimation and moving object detection using a monocular camera. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 4, pp. 610–613. IEEE (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Stephanie Lessmann
    • 1
    • 3
    Email author
  • Jens Westerhoff
    • 1
    • 2
  • Mirko Meuter
    • 1
  • Josef Pauli
    • 3
  1. 1.Delphi Electronics and SafetyWuppertalGermany
  2. 2.University of WuppertalWuppertalGermany
  3. 3.University of Duisburg-EssenDuisburgGermany

Personalised recommendations