Abstract
A novel tracking algorithm that can track a highly non-rigid target robustly is proposed using a new bounding box representation called the Double Bounding Box (DBB). In the DBB, a target is described by the combination of the Inner Bounding Box (IBB) and the Outer Bounding Box (OBB). Then our objective of visual tracking is changed to find the IBB and OBB instead of a single bounding box, where the IBB and OBB can be easily obtained by the Dempster-Shafer (DS) theory. If the target is highly non-rigid, any single bounding box cannot include all foreground regions while excluding all background regions. Using the DBB, our method does not directly handle the ambiguous regions, which include both the foreground and background regions. Hence, it can solve the inherent ambiguity of the single bounding box representation and thus can track highly non-rigid targets robustly. Our method finally finds the best state of the target using a new Constrained Markov Chain Monte Carlo (CMCMC)-based sampling method with the constraint that the OBB should include the IBB. Experimental results show that our method tracks non-rigid targets accurately and robustly, and outperforms state-of-the-art methods.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR (2006)
Avidan, S.: Ensemble tracking. PAMI 29(2), 261–271 (2007)
Babenko, B., Yang, M., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)
Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: CVPR (2012)
Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: CVPR (1998)
Cehovin, L., Kristan, M., Leonardis, A.: An adaptive coupled-layer visual model for robust visual tracking. In: ICCV (2011)
Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. PAMI 27(10), 1631–1643 (2005)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: CVPR (2000)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Statist. 38(2), 325–339 (1967)
Faux, F., Luthon, F.: Robust face tracking using colour dempster-shafer fusion and particle filter. In: FUSION (2006)
Glenn, S.: A mathematical theory of evidence. Princeton University Press (1976)
Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. In: ICCV (2011)
Han, B., Davis, L.: On-line density-based appearance modeling for object tracking. In: ICCV (2005)
Hare, S., Saffari, A., Torr, P.H.S.: Struck: Structured output tracking with kernels. In: ICCV (2011)
Isard, M., Blake, A.: ICONDENSATION: Unifying low-level and high-level tracking in a stochastic framework. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 893–908. Springer, Heidelberg (1998)
Jepson, A.D., Fleet, D.J., Maraghi, T.F.E.: Robust online appearance models for visual tracking. PAMI 25(10), 1296–1311 (2003)
Jia, X., Lu, H., Yang, M.H.: Visual tracking via adaptive structural local sparse appearance model. In: CVPR (2012)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. PAMI 34(7), 1409–1422 (2012)
Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. PAMI 27(11), 1805–1918 (2005)
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR (2010)
Kwon, J., Lee, K.M.: Tracking by sampling trackers. In: ICCV (2011)
Kwon, J., Lee, K.M.: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In: CVPR (2009)
Li, X., Dick, A., Shen, C., Zhang, Z., van den Hengel, A., Wang, H.: Visual tracking with spatio-temporal dempstershafer information fusion. TIP (2013)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: ICCV (2009)
Munoz-Salinas, R., Medina-Carnicer, R., Madrid-Cuevas, F., Carmona-Poyato, A.: Multi-camera people tracking using evidential filters. Ann. Math. Statist. 50, 732–749 (2009)
Nejhum, S.M.S., Ho, J., Yang, M.H.: Visual tracking with histograms and articulating blocks. In: CVPR (2008)
Pang, Y., Ling, H.: Finding the best from the second bests- inhibiting subjective bias in evaluation of visual tracking algorithms. In: ICCV (2013)
Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 661–675. Springer, Heidelberg (2002)
Ramanan, D., Forsyth, D., Zisserman, A.: Tracking people by learning their appearance. PAMI 29(1), 65–81 (2007)
Ross, D.A., Lim, J., Lin, R., Yang, M.: Incremental learning for robust visual tracking. IJCV 77(1), 125–141 (2008)
Santner, J., Leistner, C., Saffari, A., Pock, T., Bischof, H.: Prost: Parallel robust online simple tracking. In: CVPR (2010)
Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking. In: CVPR (2012)
Smith, K., Gatica-Perez, D., Odobez, J.M.: Using particles to track varying numbers of interacting people. In: CVPR (2005)
Stalder, S., Grabner, H., Van Gool, L.: Cascaded confidence filtering for improved tracking-by-detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 369–382. Springer, Heidelberg (2010)
Stenger, B., Woodley, T., Cipolla, R.: Learning to track with multiple observers. In: CVPR (2009)
Stolkin, R., Florescu, I., Baron, M., Harrier, C., Kocherov, B.: Efficient visual servoing with the abcshift tracking algorithm. In: ICRA (2008)
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. 38(4) (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kwon, J., Roh, J., Lee, K.M., Van Gool, L. (2014). Robust Visual Tracking with Double Bounding Box Model. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8689. Springer, Cham. https://doi.org/10.1007/978-3-319-10590-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-10590-1_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10589-5
Online ISBN: 978-3-319-10590-1
eBook Packages: Computer ScienceComputer Science (R0)