Abstract
Estimating the optical flow robustly in real-time is still a challenging issue as revealed by current KITTI benchmarks. We propose an original two-step method for fast and performant optical flow estimation from stereo vision. The first step is the prediction of the flow due to the ego-motion, efficiently conducted by stereo-matching and visual odometry. The correction step estimates the motion of mobile objects. Algorithmic choices are justified by empirical studies on real datasets. Our method achieves framerate processing on images of realistic size, and provides results comparable or better than methods having computation times one or two orders of magnitude higher.
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
Similar content being viewed by others
References
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. PAMI 33(3), 500–513 (2001)
Chakrabarti, A., Xiong, Y., Gortler, S.J., Zickler, T.: Low-level vision by consensus in a spatial hierarchy of regions. In: CVPR (2015)
Derome, M., Plyer, A., Sanfourche, M., Le Besnerais, G.: Real-time mobile object detection using stereo. In: ICARCV (2014)
Derome, M., Plyer, A., Sanfourche, M., Le Besnerais, G.: Moving object detection in real-time using stereo from a mobile platform. Unmanned Syst. 3(4), 253–266 (2015)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. ACM 24(6), 381–395 (1981)
Gehrig, S.K., Eberli, F., Meyer, T.: A real-time low-power stereo vision engine using semi-global matching. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 134–143. Springer, Heidelberg (2009)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)
Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 25–38. Springer, Heidelberg (2011)
Hirschmller, H.: Stereo processing by semiglobal matching and mutual information. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)
Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981)
Le Besnerais, G., Champagnat, F.: Dense optical flow by iterative local window registration. In: ICIP (2005)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI, pp. 674–679 (1981)
Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: CVPR (2015)
Müller, T., Rannacher, J., Rabe, C., Franke, U.: Feature- and depth-supported modified total variation optical flow for 3D motion field estimation in real scenes. In: CVPR (2011)
Plyer, A., Le Besnerais, G., Champagnat, F.: Massively parallel Lucas Kanade optical flow for real-time video processing applications. J. Real-Time Image Proc. 11(4), 713–730 (2016)
Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: EpicFlow: edge-preserving interpolation of correspondences for optical flow. In: CVPR (2015)
Sanfourche, M., Vittori, V., Le Besnerais, G.: eVO: a realtime embedded stereo odometry for MAV applications. In: IROS (2013)
Sizintsev, M., Kuthirummal, S., Samarasekera, S., Kumar, R., Sawhney, H.S., Chaudhry, A.: GPU accelerated realtime stereo for augmented reality. In: 3DPVT (2010)
Vogel, C., Schindler, K., Roth, S.: 3D scene flow estimation with a piecewise rigid scene model. Int. J. Comput. Vis. 115, 1 (2015)
Wedel, A., Brox, T., Vaudrey, T., Rabe, C., Franke, U., Cremers, D.: Stereoscopic scene flow computation for 3D motion understanding. Int. J. Comput. Vis. 95(1), 29–51 (2011)
Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An improved algorithm for TV-L \(^1\) optical flow. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 23–45. Springer, Heidelberg (2009)
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: Deepflow: large displacement optical flow with deep matching. In: ICCV (2013)
Yamaguchi, K., McAllester, D., Urtasun, R.: Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 756–771. Springer, Heidelberg (2014)
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)
Acknowledgment
This work was sponsored by the Direction Générale de l’Armement (DGA) of the French Ministry of Defense.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Derome, M., Plyer, A., Sanfourche, M., Le Besnerais, G. (2016). A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-45886-1_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45885-4
Online ISBN: 978-3-319-45886-1
eBook Packages: Computer ScienceComputer Science (R0)