Abstract
The drifting problem of object tracking is efficiently alleviated in our research. In this paper, an advanced compressive tracking algorithm based on a weighted classifier boosted by feature selection is proposed. The compressed features with high discrimination are selected from the target information of previous and current frames by a discrimination evaluating strategy. These discriminating features are used to train a weighted classifier, which is composed of two sub-classifiers based on previous and current samples bags. Finally, the weighted classifier is used to tell the target object from the background. Experimental results show that the performance in terms of accuracy and robustness hugely improves in tracking via the proposed classification method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, K., Zhang, L., Yang, M.H.: Real-time compressive tracking. In: ECCV 2012. LNCS, vol. 7583, pp. 864–877. Springer, Heidelberg (2012)
Zhang, K., Zhang, L., Yang, M.H.: Fast compressive tracking. IEEE Trans. Pattern Anal. Mach. Intell. 36(10), 2002–2015 (2014)
Zhang, J., Ma, S., Sclaroff, S.: MEEM: robust tracking via multiple experts using entropy minimization. In: ECCV 2014. LNCS, vol. 8689, pp. 188–203. Springer, Heidelberg (2014)
Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.H.: Fast visual tracking via dense spatio-temporal context learning. In: ECCV 2014. LNCS, vol. 8689, pp. 127–141. Springer, Heidelberg (2014)
Danelljan, M., Khan, F.S., Felsberg, M., Joost, V.D.W.: Adaptive color attributes for real-time visual tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1090–1097. IEEE Computer Society, Washington DC (2014)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: IEEE 12th International Conference on Computer Vision (ICCV), pp. 1436–1443. IEEE Computer Society, Washington DC (2009)
Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: IEEE Conference on Computer vision and pattern recognition (CVPR), pp. 2411–2418. IEEE Computer Society, Washington DC (2013)
Acknowledgements
This work is partly supported by the National Natural Science Fund (NSFC61572329) and Shenzhen Internet industry to develop special fund (C201005250085A, JCYJ20130329105356543).
Ethical approval
All applicable institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zhang, L., Chen, H., Hu, Y. (2016). Compressive Tracking via Weighted Classification Boosted by Feature Selection. In: Hussain, A. (eds) Electronics, Communications and Networks V. Lecture Notes in Electrical Engineering, vol 382. Springer, Singapore. https://doi.org/10.1007/978-981-10-0740-8_16
Download citation
DOI: https://doi.org/10.1007/978-981-10-0740-8_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0738-5
Online ISBN: 978-981-10-0740-8
eBook Packages: EngineeringEngineering (R0)