Abstract
In this paper, we present a new robust visual tracking approach that incorporates an adaptive metric learning in a multiple components framework. Using a similar overall approach to other state-of-the-art tracking methods, which pose object tracking as a binary classification problem, we firstly employ a new feature selection mechanism based on adaptive metric learning for constructing a discriminative target appearance model and then propose a scheme to update the appearance model in a Multiple Component Learning boosting manner, which automatically learns individual component classifiers and combines these into an overall classifier. Experiments on several challenging benchmark video sequences demonstrate the effectiveness and robustness of our proposed method.
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
Preview
Unable to display preview. Download preview PDF.
References
Viola, P., Platt, J., Zhang, C.: Multiple Instances Boosting for Object Detection. In: Neural Information Processing Systems, pp. 1417–1426 (2005)
Babenko, B., Yang, M., Belongie, S.: Visual Tracking with Online Multiple Instance Learning. In: Computer Vision and Pattern Recognition, CVPR (2009)
Dietterich, T., Lathrop, R., Perez, L.: Solving the Multiple Instance Problem with Axis-Parallel Rectangle. Artificial Intelligence 89, 31–71 (1997)
Dollár, P., Babenko, B., Belongie, S., Perona, P., Tu, Z.: Multiple Component Learning for Object Detection. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 211–224. Springer, Heidelberg (2008)
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1296–1311 (2003)
Mei, X., Ling, H.: Robust visual tracking using? 1 minimization. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1436–1443. IEEE (2009)
Ross, D., Lim, J., Lin, R.S., Yang, M.H.: Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision 77(1), 125–141 (2008)
Avidan, S.: Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 261–271 (2007)
Grabner, H., Grabner, M., Bischof, H.: Real-time tracking via on-line boosting. In: Proc. BMVC, vol. 1, pp. 47–56 (2006)
Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. International Journal of Computer Vision 26(1), 63–84 (1998)
Adam, A., Rivlin, E.: Robust Fragments-based Tracking Using the Integral Histogram. In: Computer Vision and Pattern Recognition (CVPR), pp. 798–805 (2006)
Wang, Q., Chen, F., Xu, W., Yang, M.H.: An experimental comparison of online object-tracking algorithms. In: SPIE Optical Engineering+ Applications, International Society for Optics and Photonics, pp. 81381A–81381A (2011)
Collins, R.T., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1631–1643 (2005)
Grabner, H., Grabner, M., Bischof, H.: Real-Time Tracking via Online Boosting. In: British Machine Vision Conference (BMVC), pp. 47–56 (2006)
Oza, N.C., Russell, S.: Online ensemble learning. University of California, Berkeley (2001)
Dalal, N., Triggs, B.: Histogram of Oriented Gradient for Human Detection. In: Computer Vision and Pattern Recognition (CVPR), pp. 886–893 (2005)
Laptev, I.: Improvements of object detection using boosted histograms. In: BMVC (2006)
Fergus, R., Perona, P., Zisserman, A.: A sparse object category model for efficient learning and exhaustive recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 380–387. IEEE (2005)
Leibe, B., Schindler, K., Cornelis, N., Van Gool, L.: Coupled object detection and tracking from static cameras and moving vehicles. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1683–1698 (2008)
Xie, Y., Qu, Y., Li, C., Zhang, W.: Online multiple instance gradient feature selection for robust visual tracking. Pattern Recognition Letters (2012)
Porikli, F.: Integral histogram: A fast way to extract histograms in cartesian spaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 829–836. IEEE (2005)
Jiang, N., Liu, W., Wu, Y.: Learning Adaptive Metric for Robust Visual Tracking. IEEE Transactions on Image Processing 20, 2288–2300 (2011)
Yang, F., Lu, H., Chen, Y.-W.: Human tracking by multiple kernel boosting with locality affinity constraints. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part IV. LNCS, vol. 6495, pp. 39–50. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bozorgtabar, B., Goecke, R. (2013). Adaptive Multiple Component Metric Learning for Robust Visual Tracking. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)