Abstract
Since there does not exist labelled samples during tracking period, most existing classification-based tracking approaches utilize a “self-learning” to online update the classifier. This often results in drift problems. Recently, semi-supervised learning attracts a lot of attentions and is incorporated into the tracking framework which collects unlabelled samples and use them to enhance the robustness of the classifier. In this paper, we develop a gradient semi-supervised learning approaches for this application. During the tracking period, the semi-supervised technology is used to online update the classifier. Experimental evaluations demonstrate the effectiveness of the proposed approach.
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. IEEE Trans. on Pattern Aalysis and Machine Intelligence, 261–271 (2007)
Chen, H., Liu, T., Fuh, C.: Probabilistic tracking with adaptive feature selection. In: Proc. of Int. Conf. on Pattern Recogntion, pp. 736–739 (2004)
Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. on Pattern Aalysis and Machine Intelligence, 1631–1643 (2005)
Davis, J., Sharma, V.: Fusion-based background subtraction using contour saliency, In: Proc. of Int. Workshop on Object Tracking and Classification Beyond the Visible Spectrum, pp. 1–8 (2005)
Grabner, H., Bischof, H.: On-line boosting and vision. In: Proc. of Int. Conf. on Computer Vision and Pattern Recognition, 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)
Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. on Systems, Man and Cybernetics, 334–352 (2004)
Li, Y., Ai, H., Yamashita, T., Lao, S., Kawade, M.: Tracking in low frame rate video: A cascade particle filter with discriminative observers of difierent lifespans, In: Proc. of Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Liu, X., Yu, T.: Gradient feature selection for online boosting. In: Proc. of Int. Conf. on Computer Vision, pp. 1–8 (2007)
Lucey, S.: Enforcing non-positive weights for stable support vector tracking. In: Proc. of Int. Conf. Copmuter Vision and Pattern Recognition, pp. 1–8 (2008)
Mallapragada, P., Jin, R., Jain, A., Liu, Y.: Semiboost: Boosting for semisupervised learning. IEEE Trans. on Pattern Aalysis and Machine Intelligence, 2000–2014 (2009)
Parag, T., Porikli, F., Elgammal, A.: Boosting adaptive linear weak classifiers for online learning and tracking. In: Proc. of Int. Conf. on Computer Vision and Pattern Recognition, pp.1–8 (2008)
Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-tracking using semi-supervised support vector machines. In: Proc. of Int. Conf. on Copmuter Vision, pp. 1–8 (2007)
Wang, J., Chen, X., Gao, W.: Online selecting discriminative tracking features using particle filter. In: Proc. of Int. Conf. on Computer Vision and Pattern Recogntion, pp. 1037–1042 (2005)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys, 1–45 (2006)
Zhang, C., Hamid, R., Zhang, Z.: Taylor expansion based classifier adaption: Application to person detection. In: Proc. of Int. Conf. on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Zhu, X.: Semi-supervised learning literature survey, Technical Report, University of Wisconsin-Madison (2005)
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
Gao, M., Liu, H., Sun, F. (2011). Visual Tracking Using Online Semi-supervised Learning. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6753. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21593-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-21593-3_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21592-6
Online ISBN: 978-3-642-21593-3
eBook Packages: Computer ScienceComputer Science (R0)