Abstract
This paper presents a new method for object tracking based on multiple kernel learning (MKL). MKL is used to learn an optimal combination of \(\mathop \chi \nolimits^2\) kernels and Gaussian kernels, each type of which captures a different feature. Our features include the color information and spatial pyramid histogram (SPH) based on global spatial correspondence of the geometric distribution of visual words. We propose a simple effective way for on-line updating MKL classifier, where useful tracking objects are automatically selected as support vectors. The algorithm handle target appearance variation, and makes better usage of history information, which leads to better discrimination of target and the surrounding background. The experiments on real world sequences demonstrate that our method can track objects accurately and robustly especially under partial occlusion and large appearance change.
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
Avidan, S.: Ensemble tracking. In: CVPR (2005)
Grabner, H., Bischof, H.: On-line boosting and vision. In: CVPR (2006)
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)
Tang, F.F., Brennan, S.: Co-tracking using semi-supervised support vector machines. In: ICCV (2007)
Babenko, B., Yang, M.-H.: Visual tracking with online multiple instance learning. In: CVPR (2009)
Yin, Z., Robert, T.: Collins object tracking and detection after occlusion via numerical hybrid local and global mode-seeking. In: CVPR (2008)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragmentsbased tracking using the integral histogram. In: CVPR (2006)
Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple kernel learning, conic duality, and the smo algorithm. In: ICML (2004)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR (2007)
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1998)
Varma, M., Ray, D.: Learning the discriminative power invariance trade off. In: ICCV (2007)
Rakotomamonjy, A., Bach, F., Canu, S.: More efficency in multiple kernel learning. In: ICML (2007)
Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: Simplemkl. Journal of Machine Learning Research 9, 2491–2521 (2008)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
Grauman, K., Darrell, T.: Pyramid match kernels: Discriminative classification with sets of image features. In: ICCV (2005)
Pontil, M., Verri, A.: Support vector machines for 3d object recognition (PAMI)
Fergus, R., Perma, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR (2003)
Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and location in images. In: ICCV (2005)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR (2007)
Bosch, A., Zisserman, A., Munoz, X.: Geometric blur for template matching. In: CVPR (2001)
Wu, B., Nevatia, R.: Simultaneous object detection and segmentation by boosting local shape feature based classifier. In: CVPR (2007)
Haibin, L., Soatto, S.: Proximity distribution kernels for geometric context in category recognition.In: ICCV (2007)
Lanckriet, G., Cristianini, N., El Ghousi, L., Bartlett, P., Jordan, M.: Learning the kernel matrix with semi-definite programming. JMLR (2004)
Lim, J., Ross, D., Lin, R.-S., Yang, M.: Incremental learning for visual tracking. In: NIPS (2004)
Matthews, I., Ishikawa, T., Baker, S.: The template update problem. PAMI 26, 810–815 (2004)
Kwon, J., Lee, K.M.: Visual tracking decomposition. In: CVPR (2010)
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
Lu, H., Zhang, W., Chen, YW. (2011). On Feature Combination and Multiple Kernel Learning for Object Tracking. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-19318-7_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19317-0
Online ISBN: 978-3-642-19318-7
eBook Packages: Computer ScienceComputer Science (R0)