Abstract
Structure-from-motion (SfM) is an important computer vision problem and largely relies on the quality of feature tracking. In image sequences, if disjointed tracks caused by objects moving in and out of the view, occasional occlusion, or image noise, are not handled well, the corresponding SfM could be significantly affected. In this paper, we address the non-consecutive feature point tracking problem and propose an effective method to match interrupted tracks. Our framework consists of steps of solving the feature ‘dropout’ problem when indistinctive structures, noise or even large image distortion exist, and of rapidly recognizing and joining common features located in different subsequences. Experimental results on several challenging and large-scale video sets show that our method notably improves SfM.
Chapter PDF
Similar content being viewed by others
References
Pollefeys, M., Nistér, D., Frahm, J.M., Akbarzadeh, A., Mordohai, P., Clipp, B., Engels, C., Gallup, D., Kim, S.J., Merrell, P., Salmi, C., Sinha, S.N., Talton, B., Wang, L., Yang, Q., Stewénius, H., Yang, R., Welch, G., Towles, H.: Detailed real-time urban 3d reconstruction from video. International Journal of Computer Vision 78, 143–167 (2008)
Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25, 835–846 (2006)
Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J.M.: Modeling and recognition of landmark image collections using iconic scene graphs. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 427–440. Springer, Heidelberg (2008)
Agarwal, S., Snavely, N., Simon, I., Seitz, S.M., Szeliski, R.: Building rome in a day. In: ICCV, pp. 72–79 (2009)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004) ISBN: 0521540518
Fitzgibbon, A., Zisserman, A.: Automatic camera tracking. In: Video Registration, pp. 18–35 (2003)
Zhang, G., Qin, X., Hua, W., Wong, T.T., Heng, P.A., Bao, H.: Robust metric reconstruction from challenging video sequences. In: CVPR (2007)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI, pp. 674–679 (1981)
Shi, J., Tomasi, C.: Good features to track. In: CVPR, pp. 593–600 (1994)
Georgescu, B., Meer, P.: Point matching under large image deformations and illumination changes. IEEE Trans. Pattern Anal. Mach. Intell. 26, 674–688 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Zach, C., Gallup, D., Frahm, J.M.: Fast gain-adaptive klt tracking on the gpu. In: CVPR Workshop on Visual Computer Vision on GPU’s (CVGPU) (2008)
Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1265–1278 (2005)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1615–1630 (2005)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vision Comput. 22, 761–767 (2004)
Brown, M., Lowe, D.G.: Recognising panoramas. In: ICCV, pp. 1218–1227 (2003)
Morel, J.M., Yu, G.: ASIFT: A new framework for fully affine invariant image comparison. SIAM J. Img. Sci. 2, 438–469 (2009)
Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: ICCV, pp. 1470–1477 (2003)
Schaffalitzky, F., Zisserman, A.: Automated location matching in movies. Computer Vision and Image Understanding 92, 236–264 (2003)
Ho, K.L., Newman, P.M.: Detecting loop closure with scene sequences. International Journal of Computer Vision 74, 261–286 (2007)
Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: CVPR (2007)
Irschara, A., Zach, C., Frahm, J.M., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: CVPR (2009)
Engels, C., Fraundorfer, F., Nistér, D.: Integration of tracked and recognized features for locally and globally robust structure from motion. In: VISAPP (Workshop on Robot Perception), pp. 13–22 (2008)
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, 381–395 (1981)
Sinha, S.N., Steedly, D., Szeliski, R.: Piecewise planar stereo for image-based rendering. In: ICCV, pp. 1881–1888 (2009)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: CVPR, Washington, DC, USA, pp. 2161–2168. IEEE Computer Society, Los Alamitos (2006)
Snavely, N.: Bundler: Structure from motion for unordered image collections, http://phototour.cs.washington.edu/bundler/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, G., Dong, Z., Jia, J., Wong, TT., Bao, H. (2010). Efficient Non-consecutive Feature Tracking for Structure-from-Motion. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-15555-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15554-3
Online ISBN: 978-3-642-15555-0
eBook Packages: Computer ScienceComputer Science (R0)