Abstract
Estimating the inter-frame motion of a free-moving camera is important for the reconstruction of large 3-D scene from one or more sequences of frames. This work focuses on scenes with a mixture of dynamic and static elements and proposes an approach to improve tracking in existing 3-D reconstruction algorithms, as well as provide a basis for new types of 3-D reconstructions that are able to construct scenes of moving objects. The main strategy adopted in this work is to group feature points within fixed block-size within the image then to prune groups whose motion deviates from the dominant motions established through majority voting. Our experiments show that the proposed approach performs well in several outdoor dynamic scenes, significantly outperforming typical feature-based and direct pose estimation techniques in footage with moving elements.
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
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)
Sünderhauf, N., Protzel, P.: Stereo odometrya review of approaches. Chemnitz University of Technology Technical Report (2007)
Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for RGB-D cameras. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3748–3754. IEEE (2013)
Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: Fast semi-direct monocular visual odometry. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22. IEEE (2014)
Howard, A.: Real-time stereo visual odometry for autonomous ground vehicles. In: IROS 2008. IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3946–3952. IEEE, 2008
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and augmented reality (ISMAR), pp. 127–136. IEEE (2011)
Kaess, M., Ni, K., Dellaert, F.: Flow separation for fast and robust stereo odometry. In: ICRA 2009. IEEE International Conference on Robotics and Automation, pp. 3539–3544. IEEE (2009)
Artieda, J., Sebastian, J.M., Campoy, P., Correa, J.F., Mondragón, I.F., MartÃnez, C., Olivares, M.: Visual 3-d SLAM from UAVs. J. Intell. Robot. Syst. 55(4), 299–321 (2009)
Kitt, B., Geiger, A., Lategahn, H.: Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme. In: Intelligent Vehicles Symposium (IV), 2010 IEEE, pp. 486–492. IEEE (2010)
Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2320–2327. IEEE (2011)
Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: Efficient perspective-n-point camera pose estimation
Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera. In: Proceedings of the IEEE International Conference On Computer Vision, pp. 1449–1456 (2013)
Konolige, K., Agrawal, M., Sola, J.: Large-scale visual odometry for rough terrain. In: Kaneko, M., Nakamura, Y. (eds.) Robotics Research, pp. 201–212. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14743-2_18
Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
Bouguet, J.-Y.: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corporation 5(1–10), 4 (2001)
Zhang, Z.: Determining the epipolar geometry and its uncertainty: a review. Int. J. Comput. Vis. 27(2), 161–195 (1998)
Gee, T., Delmas, P., Stones-Havas, N., Sinclair, C., Van Der Mark, W., Li, W., Friedrich, H., Gimel’farb, G,: Tsai camera calibration enhanced. In: 2015 14th IAPR International Conference on Machine Vision Applications (MVA), pp. 435–438. IEEE (2015)
Fusiello, A., Trucco, E., Verri, A.: A compact algorithm for rectification of stereo pairs. Mach. Vis. Appl. 12(1), 16–22 (2000)
Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuits Syst. Video Technol. 19(7), 1073–1079 (2009)
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 28(1), 100–108 (1979)
Arun, S.K., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-d point sets. IEEE Trans. Pattern Anal. Mach. Intell. 5, 698–700 (1987)
Moré, J.J.: The levenberg-marquardt algorithm: implementation and theory. In: Watson, G.A. (ed.) Numerical Analysis. LNM, vol. 630, pp. 105–116. Springer, Heidelberg (1978). https://doi.org/10.1007/BFb0067700
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
Gee, T., Gong, R., Delmas, P., Gimel’farb, G. (2017). Robust Tracking in Weakly Dynamic Scenes. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-70353-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70352-7
Online ISBN: 978-3-319-70353-4
eBook Packages: Computer ScienceComputer Science (R0)