Abstract
In this paper, we incorporate the concept of Multiple Kernel Learning (MKL) algorithm, which is used in object categorization, into human tracking field. For efficiency, we devise an algorithm called Multiple Kernel Boosting (MKB), instead of directly adopting MKL. MKB aims to find an optimal combination of many single kernel SVMs focusing on different features and kernels by boosting technique. Besides, we apply Locality Affinity Constraints (LAC) to each selected SVM. LAC is computed from the distribution of support vectors of respective SVM, recording the underlying locality of training data. An update scheme to reselect good SVMs, adjust their weights and recalculate LAC is also included. Experiments on standard and our own testing sequences show that our MKB tracking outperforms some other state-of-the-art algorithms in handling various conditions.
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
Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1631–1643 (2005)
Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR, vol. 1, pp. 260–267 (2006)
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)
Avidan, S.: Support vector tracking. PAMI 26(8), 1064–1072 (2004)
Avidan, S.: Ensemble tracking. In: CVPR, vol. 2, pp. 494–501 (2005)
Tian, M., Zhang, W., Liu, F.: On-line ensemble svm for robust object tracking. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 355–364. Springer, Heidelberg (2007)
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR, pp. 983–990 (2009)
Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: SimpleMKL. Journal of Machine Learning Research 9, 2491–2521 (2008)
Sonnenburg, S., Ratsch, G., Schafer, C., Scholkopf, B.: Large scale multiple kernel learning. Journal of Machine Learning Research 7, 1531–1565 (2006)
Bach, F., Lanckriet, G., Jordan, M.: Multiple kernel learning, conic duality, and the SMO algorithm. In: ICML (2004)
Varma, M., Ray, D.: Learning the discriminative power-invariance trade-off. In: ICCV (2007)
Kumar, A., Sminchisescu, C.: Support kernel machines for object recognition. In: ICCV (2007)
Gonen, M., Alpaydin, E.: Localized multiple kernel learning. In: ICML, pp. 352–359 (2008)
Christoudias, M., Urtasun, R., Darrell, T.: Bayesian localized multiple kernel learning. Technical Report UCB/EECS-2009-96, EECS Department, University of California, Berkeley (2009)
Cao, L., Luo, J., Liang, F., Huang, T.: Heterogeneous Feature Machines for Visual Recognition. In: ICCV, pp. 1095–1102 (2009)
Yang, J., Li, Y., Tian, Y., Duan, L., Gao, W.: Group-Sensitive Multiple Kernel Learning for Object Categorization. In: ICCV (2009)
Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: ICCV 2009, pp. 221–228 (2009)
Siddiquie, B., Vitaladevuni, S., Davis, L.: Combining multiple kernels for efficient image classification. In: WACV 2009, pp. 1–8 (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)
Lowe, D.: Object recognition from local scale-invariant features. In: ICCV, pp. 1150–1157 (1999)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: Object tracking with an adaptive color-based particle filter. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 353–360. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, F., Lu, H., Chen, YW. (2011). Human Tracking by Multiple Kernel Boosting with Locality Affinity Constraints. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-19282-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19281-4
Online ISBN: 978-3-642-19282-1
eBook Packages: Computer ScienceComputer Science (R0)