Abstract
In this paper, we analyze and modify the Motion-Split-and-Merge (MSAM) algorithm [3] for the motion segmentation of correspondences between two frames. Our goal is to make the algorithm suitable for practical use which means realtime processing speed at very low error rates. We compare our (robust realtime) RMSAM with J-Linkage [16] and Graph-Based Segmentation [5] and show that it is superior to both. Applying RMSAM in a multi-frame motion segmentation context to the Hopkins 155 benchmark, we show that compared to the original formulation, the error decreases from 2.05% to only 0.65% at a runtime reduced by 72%. The error is still higher than the best results reported so far, but RMSAM is dramatically faster and can handle outliers and missing data.
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References
Brox, T., Malik, J.: Object segmentation by long term analysis of point trajectories. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 282–295. Springer, Heidelberg (2010)
Cheriyadat, A.M., Radke, R.J.: Non-negative matrix factorization of partial track data for motion segmentation. In: ICCV, pp. 865–872 (October 2009)
Dragon, R., Rosenhahn, B., Ostermann, J.: Multi-scale clustering of frame-to-frame correspondences for motion segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 445–458. Springer, Heidelberg (2012)
Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: CVPR, pp. 2790–2797 (2009)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Intl. Journal of Computer Vision 59(2) (September 2004)
Fischler, M.A., Bolles, R.C.: Random sample consensus. Commun. ACM 24, 381–395 (1981)
Fradet, M., Robert, P., Perez, P.: Clustering point trajectories with various life-spans. In: CVMP, pp. 7–14 (2009)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: NIPS, vol. 14, pp. 849–856 (2002)
Ochs, P., Brox, T.: Object segmentation in video: A hierarchical variational approach for turning point trajectories into dense regions. In: ICCV (2011)
Ochs, P., Brox, T.: Higher order models and spectral clustering. In: CVPR (2012)
Prest, A., Leistner, C., Civera, J., Schmid, C., Ferrari, V.: Learning object class detectors from weakly annotated video. In: CVPR (June 2012)
Rao, S., Tron, R., Vidal, R., Ma, Y.: Motion segmentation in the presence of outlying, incomplete, or corrupted trajectories. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) 32, 1832–1845 (2010)
Schindler, K., Suter, D.: Two-view multibody structure-and-motion with outliers. In: CVPR (2005)
Stalder, S., Grabner, H., Van Gool, L.: Dynamic objectness for adaptive tracking. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part III. LNCS, vol. 7726, pp. 43–56. Springer, Heidelberg (2013)
Stewart, C.V.: Bias in robust estimation caused by discontinuities and multiple structures. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) 19, 818–833 (1997)
Toldo, R., Fusiello, A.: Robust multiple structures estimation with J-linkage. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 537–547. Springer, Heidelberg (2008)
Torr, P.H.S.: Geometric motion segmentation and model selection. Phil. Trans. Mathematical, Physical and Engineering Sciences 356(1740), 1321–1340 (1998)
Tron, R., Vidal, R.: A benchmark for the comparison of 3D motion segmentation algorithms. In: CVPR (2007)
Wills, J., Agarwal, S., Belongie, S.: A feature-based approach for dense segmentation and estimation of large disparity motion. Intl. Journal of Computer Vision 68(2), 125–143 (2006)
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Dragon, R., Ostermann, J., Van Gool, L. (2013). Robust Realtime Motion-Split-And-Merge for Motion Segmentation. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_45
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DOI: https://doi.org/10.1007/978-3-642-40602-7_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40601-0
Online ISBN: 978-3-642-40602-7
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