Abstract
In this paper, we propose a novel visual tracking method based on ensemble learning using logistic regression model. We adopt logistic regression to achieve ensemble classifier to deal with object tracking problem. By using fast computable features, our approach learns the appearance of the target during tracking. And thus, the proposed method is able to adapt online to target appearance changes and its surrounding background. Moreover, ensemble learning converts rough rules of thumb into highly accurate prediction rule. Experimental results show that our method outperforms relative trackers.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant 61571342, 61573267, 61473215; by the National Basic Research Program of China under Grant 2013CB329402.
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Tian, X., Zhao, S., Jiao, L. (2016). Visual Tracking Based on Ensemble Learning with Logistic Regression. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_44
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DOI: https://doi.org/10.1007/978-981-10-3611-8_44
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