Abstract
In the field of data mining, detecting concept drift in a data stream is an important research area with many applications. However the effective methods for concept drift detection are seldom used in visual tracking in which drifting problems appear frequently. In this paper, we present a novel framework combining concept drift detection with an online semi-supervised boosting method to build a robust visual tracker. The main idea is converting updated templates to a data stream by similarity learning and detecting concept drift. The proposed tracker is both robust against drifting and adaptive to appearance changes. Numerous experiments on various challenging videos demonstrate that our technique achieves high accuracy in real-world scenarios.
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
Delany, S.J., Cunningham, P., Tsymbal, A., Coyle, L.: A case-based technique for tracking concept drift in spam filtering. Knowledge-Based Systems 18(4), 187–195 (2005)
Cunningham, P., Nowlan, N., Delany, S.J., Haahr, M.: A case-based approach to spam filtering that can track concept drift. In: The ICCBR, pp. 3–16 (2003)
Chen, L., Zhou, Y., Yang, J.: Object tracking within the framework of concept drift. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part III. LNCS, vol. 7726, pp. 152–162. Springer, Heidelberg (2013)
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)
Kumar Mallapragada, P., Jin, R., Jain, A.K., Liu, Y.: Semiboost: Boosting for semi-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(11), 2000–2014 (2009)
Leistner, C., Grabner, H., Bischof, H.: Semi-supervised boosting using visual similarity learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Batu, T., Fortnow, L., Rubinfeld, R., Smith, W.D., White, P.: Testing that distributions are close. In: Proceedings of the 41st Annual Symposium on Foundations of Computer Science, pp. 259–269. IEEE (2000)
Grabner, H., Bischof, H.: On-line boosting and vision. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 260–267. IEEE (June 2006)
Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012)
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 983–990. IEEE (June 2009)
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
Liu, Y., Zhou, Y. (2014). Online Detection of Concept Drift in Visual Tracking. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-12643-2_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
Online ISBN: 978-3-319-12643-2
eBook Packages: Computer ScienceComputer Science (R0)