Abstract
It is well known that the backgrounds or the targets always change in real scenes, which weakens the effectiveness of classical tracking algorithms because of frequent model mismatches. In this paper, an object tracking algorithm within the framework of concept drift is proposed to solve this problem. We detect the driftpoints using a simple message-passing algorithm based on Bayesian Approach. The analyzed probability distribution lays the foundation for the self-adaption of our new model. Our tracking algorithm within the framework of concept drift improves the tracking robustness and accuracy which is illustrated by the two experiments on two real-world changing scenes.
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
Thayananthan, A., Iwasaki, M., Cipolla, R.: Principled fusion of high-level model and low-level cues for motion segmentation. In: IEEE Conference Vision and Pattern Recognition (2008)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)
Pavlovic, V., Rehg, J., Cham, T., Murphy, K.: A dynamic Bayesian network approach to figure tracking using learned dynamic models. In: IEEE Conference on Computer Vision (1999)
Fearnhead, P.: Exact and efficient Bayesian inference for multiple chagepoint problems. Statistic and Computing 16(2), 203–213 (2006)
Fearnhead, P., Liu, Z.: Online inference for multiple changepoint problems. Journal of the Royal Statistical Society: Series B 69(4), 589–605 (2007)
Bach, S.H., Maloof, M.A.: A Bayesian approach to concept drift. The Neural Information Processing System (2010)
Adams, R.P., Mackay, D.J.C.: Bayesian online changepoint detection. Technical Report, University of Cambridge (2007)
Barry, D., Hartigan, J.A.: Product partition models for change point problems. The Annals of Statistics 20, 260–279 (1992)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: An adaptive color-based particle filter. Image and Vision Computing 21(2), 99–110 (2003)
Barry, D., Hartigan, J.A.: A Bayesian Analysis for Change Point Problems. Journal of the American Statistical Association 35(3), 309–319 (1993)
Bach, S.H., Maloof, M.A.: Paired learners for concept drift. In: Proceedings of the Eighth IEEE International Conference on Data Mining. IEEE Press (2008)
Doucet, A., Godsill, S.: On sequential Monte Carlo methods for Bayesian filtering. Statistics and Computing 10, 197–208 (2000)
Ekinci, M.: Human Identification using gait. Turk. J. Elec. Engin. 14(2), 267–291 (2006)
Wang, L., Tan, T.: Silhouette analysis-based gait recognition for human identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1505–1518 (2003)
Yam, C.Y., Nixon, M.S., Carter, J.N.: Gait recognition by walking and running: a model-based approach. In: The 5th Asian Conference on Computer Vision (2002)
Bouchrika, I., Carter, J.N., Nixon, M.S., Thallinger, G.: Using gait features for improving walking people detection. In: International Conference on Pattern Recognition (2010)
de la Torre, F., Gong, S., McKenna, S.: View-Based Adaptive Affine Tracking. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 828–842. Springer, Heidelberg (1998)
Zhou, S.H.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition usingappearance-adaptive models in particle filters. IEEE Transactions on Image Processing 13(11), 1491–1506 (2004)
Lim, J., Ross, D., Lin, R.S., et al.: Incremental learning for visual tracking. In: Saul, L., et al. (eds.) Advances in Neural Information Processing Systems. MIT Press (2005)
Skočaj, D.: Robust subspace approaches to visual learning and recognition. PhD thesis, Computer and Information Science, University of Ljubljana (2003)
Ross, A.D., Lim, J., Lin, R.-S., Yang, M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision 77(1-3) (2008)
Pavlovic, V., Rehg, J.M., MacCormick, J.: Learning Switching Linear Models of Human Motion. In: Neural Information Processing Systems, pp. 981–987 (2001)
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
Chen, L., Zhou, Y., Yang, J. (2013). Object Tracking within the Framework of Concept Drift. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-37431-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37430-2
Online ISBN: 978-3-642-37431-9
eBook Packages: Computer ScienceComputer Science (R0)