Abstract
In this paper we present an in-depth evaluation of a recently published tracking algorithm [6] which intelligently couples rigid-registration and color-based segmentation using level-sets. The original method did not arouse the deserved interest in the community, most likely due to challenges in reimplementation and the lack of a quantitative evaluation. Therefore, we reimplemented this baseline approach, evaluated it on state-of-the-art datasets (VOT and OOT) and compared it to alternative segmentation-based tracking algorithms. We believe this is a valuable contribution as such a comparison is missing in the literature. The impressive results help promoting segmentation-based tracking algorithms, which are currently under-represented in the visual tracking benchmarks. Furthermore, we present various extensions to the color model, which improve the performance in challenging situations such as confusions between fore- and background. Last, but not least, we discuss implementation details to speed up the computation by using only a sparse set of pixels for the propagation of the contour, which results in tracking speed of up to 200 Hz for typical object sizes using a single core of a standard 2.3 GHz CPU.
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
Notes
- 1.
Sequences were kindly provided by Esther Horbert from the Computer Vision Group, RWTH Aachen University.
- 2.
- 3.
No official publication available. Only a brief abstract in [19].
References
Aeschliman, C., Park, J., Kak, A.: A probabilistic framework for joint segmentation and tracking. In: CVPR (2010)
Avidan, S.: Ensemble tracking. PAMI 29, 261–271 (2007)
Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern Information Retrieval. ACM press, New York (1999)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: a unifying framework. IJCV 56, 221–255 (2004)
Belagiannis, V., Schubert, F., Navab, N., Ilic, S.: Segmentation based particle filtering for real-time 2D object tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 842–855. Springer, Heidelberg (2012)
Bibby, C., Reid, I.: Robust real-time visual tracking using pixel-wise posteriors. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 831–844. Springer, Heidelberg (2008)
Chen, D., Yuan, Z., Hua, G., Wu, Y., Zheng, N.: Description-discrimination collaborative tracking. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 345–360. Springer, Heidelberg (2014)
Chockalingam, P., Pradeep, N., Birchfield, S.: Adaptive fragments-based tracking of non-rigid objects using level sets. In: ICCV (2009)
Dinh, T., Vo, N., Medioni, G.: Context tracker: exploring supporters and distracters in unconstrained environments. In: CVPR (2011)
Duffner, S., Garcia, C.: Pixeltrack: a fast adaptive algorithm for tracking non-rigid objects. In: ICCV (2013)
Felsberg, M.: Enhanced distribution field tracking using channel representations. In: ICCVW (2013)
Godec, M., Roth, P., Bischof, H.: Hough-based tracking of non-rigid objects. In: ICCV (2011)
Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)
Hare, S., Saffari, A., Torr, P.: Struck: structured output tracking with kernels. In: ICCV (2011)
He, S., Yang, Q., Lau, R., Wang, J., Yang, M.H.: Visual tracking via locality sensitive histograms. In: CVPR (2013)
Javed, O., Ali, S., Shah, M.: Online detection and classification of moving objects using progressively improving detectors. In: CVPR (2005)
Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)
Kalal, Z., Matas, J., Mikolajczyk, K.: Pn learning: bootstrapping binary classifiers by structural constraints. In: CVPR (2010)
Kristan, M. et al.: The visual object tracking vot2013 challenge results. In: ICCVW (2013)
Kristan, M., Pflugfelder, R., Leonardis, A., Matas, J., Čehovin, L., Nebehay, G., Vojíř, T., Fernández, G., et al.: The visual object tracking VOT2014 challenge results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014 Workshops. LNCS, vol. 8926, pp. 191–217. Springer, Heidelberg (2015)
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., Hengel, A.V.D.: A survey of appearance models in visual object tracking. TIST 4, 1–58 (2013)
Mitzel, D., Horbert, E., Ess, A., Leibe, B.: Multi-person tracking with sparse detection and continuous segmentation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 397–410. Springer, Heidelberg (2010)
Papoutsakis, K., Argyros, A.: Integrating tracking with fine object segmentation. Image Vis. Comput. 31, 771–785 (2013)
Paragios, N., Deriche, R.: Geodesic active contours and level sets for the detection and tracking of moving objects. PAMI 22, 266–280 (2000)
Tsai, D., Flagg, M., Rehg, J.M.: Motion coherent tracking with multi-label mrf optimization. In: BMVC (2010)
Unger, M., Mauthner, T., Pock, T., Bischof, H.: Tracking as segmentation of spatial-temporal volumes by anisotropic weighted TV. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds.) EMMCVPR 2009. LNCS, vol. 5681, pp. 193–206. Springer, Heidelberg (2009)
Vojíř, T., Matas, J.: Robustifying the flock of trackers. In: CVWW (2011)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR (2013)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. Acm Comput. Surv. (CSUR) 38, 1–45 (2006)
Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: CVPR (2012)
Acknowledgments
We thank Esther Horbert (Computer Vision Group RWTH Aachen University) for providing four evaluation sequences and valuable feedback for resolving open questions on the hidden details of [6].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Schubert, F., Casaburo, D., Dickmanns, D., Belagiannis, V. (2015). Revisiting Robust Visual Tracking Using Pixel-Wise Posteriors. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-20904-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20903-6
Online ISBN: 978-3-319-20904-3
eBook Packages: Computer ScienceComputer Science (R0)