Abstract
In this paper, the usage of the monocular epipolar geometry for the calculation of optical flow is investigated. We derive the necessary formulation to use the epipolar constraint for the calculation of differential optical flow using the total variational model in a multi-resolution pyramid scheme. Therefore, we minimize an objective function which contains the epipolar constraint with a residual function based on different types of descriptors (brightness, HOG, CENSUS and MLDP). For the calculation of epipolar lines, the relevant fundamental matrices are calculated based on the 7- and 8- point methods. Moreover, SIFT and Lukas-Kanade methods are used to obtain matched features between two consecutive frames, by which fundamental matrices can be calculated. The effect of using different combination of the feature matching methods, fundamental matrix calculation and descriptors are evaluated based on the KITTI 2012 dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bernd, K., Henning, L.: Trinocular optical flow estimation for intelligent vehicle applications. In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, September 2012
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24673-2_3
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. (IJRR) 32, 1231–1237 (2013)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, New York (2003)
Kennedy, R., Taylor, C.J.: Optical flow with geometric occlusion estimation and fusion of multiple frames. In: Tai, X.-C., Bae, E., Chan, T.F., Lysaker, M. (eds.) EMMCVPR 2015. LNCS, vol. 8932, pp. 364–377. Springer, Cham (2015). doi:10.1007/978-3-319-14612-6_27
Mohamed, M., Mertsching, B.: TV-L1 optical flow estimation with image details recovering based on modified census transform. Adv. Vis. Comput. 7431, 482–491 (2012)
Mohamed, M.A., Mirabdollah, M.H., Mertsching, B.: Differential optical flow estimation under monocular epipolar line constraint. In: Nalpantidis, L., Krüger, V., Eklundh, J.-O., Gasteratos, A. (eds.) ICVS 2015. LNCS, vol. 9163, pp. 354–363. Springer, Cham (2015). doi:10.1007/978-3-319-20904-3_32
Mohamed, M.A., Boddeker, C., Mertsching, B.: Real-time moving objects tracking for mobile-robots using motion information. In: 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–6. IEEE (2014)
Mohamed, M.A., Rashwan, H.A., Mertsching, B., Garcia, M.A., Puig, D.: On improving the robustness of variational optical flow against illumination changes. In: Proceedings of the 4th ACM/IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Stream, pp. 1–8. ACM (2013)
Mohamed, M.A., Rashwan, H.A., Mertsching, B., Garcia, M.A., Puig, D.: Illumination-robust optical flow using a local directional pattern. IEEE Trans. Circuits Syst. Video Technol. 24, 1–9 (2014)
Müller, T., Rabe, C., Rannacher, J., Franke, U., Mester, R.: Illumination-robust dense optical flow using census signatures. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 236–245. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23123-0_24
Ranftl, R., Vineet, V., Chen, Q., Koltun, V.: Dense monocular depth estimation in complex dynamic scenes. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4058–4066, June 2016
Rashwan, H.A., Mohamed, M.A., GarcĂa, M.A., Mertsching, B., Puig, D.: Illumination robust optical flow model based on histogram of oriented gradients. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 354–363. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40602-7_38
Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: Epicflow: edge-preserving interpolation of correspondences for optical flow. In: The IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12, June, pp. 1164–1172. IEEE (2015)
Taniai, T., Sinha, S., Sato, Y.: Fast multi-frame stereo scene flow with motion segmentation. In: The IEEE Computer Vision and Pattern Recognition (CVPR). IEEE, March 2017
Valgaerts, L., Bruhn, A., Weickert, J.: A variational model for the joint recovery of the fundamental matrix and the optical flow. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 314–324. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69321-5_32
Yamaguchi, K., McAllester, D.A., Urtasun, R.: Robust monocular epipolar flow estimation. In: The IEEE Computer Vision and Pattern Recognition (CVPR), pp. 1862–1869. IEEE (2013)
Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L 1 optical flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214–223. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74936-3_22
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Mohamed, M.A., Mirabdollah, M.H., Mertsching, B. (2017). Monocular Epipolar Constraint for Optical Flow Estimation. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-68345-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68344-7
Online ISBN: 978-3-319-68345-4
eBook Packages: Computer ScienceComputer Science (R0)