Object Tracking Based on Mean Shift Algorithm and Kernelized Correlation Filter Algorithm
In order to solve the problems of motion blur and fast motion, a new robust object tracking algorithm using the Kernelized Correlation Filters (KCF) and the Mean Shift (MS) algorithm, called KCFMS is presented in this paper. The object tracking process can be described as: First, we give the initial position and size of the object and use the Mean Shift algorithm to obtain the position of the object. Second, the Kernelized Correlation Filtering algorithm is used to obtain the position of the object in the same frame. Third, we use the cross update strategy to update the object models. In order to improve the tracking speed as much as possible, our object tracking algorithm works only over one layer. This hybrid algorithm has a good tracking effect on the target fast motion and motion blur. We present extensive experimental results on a number of challenging sequences in terms of efficiency, accuracy and robustness.
KeywordsKernelized Correlation Filters Mean shift Motion blur Fast motion
This work is partially supported by the National Natural Science Foundation of China (61402310). Natural Science Foundation of Jiangsu Province of China (BK20141195).
- 2.Lasserre, J.A., Bishop, C.M., Minka, T.P.: Principled hybrids of generative and discriminative models. In: 19th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 87–94. IEEE Computer Society, New York (2006)Google Scholar
- 3.Ng, A., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and Naive Bayes. In: Proceedings of Advances in Neural Information Processing, vol. 28, no. 3, pp. 169–187 (2001)Google Scholar
- 4.Lin, R.S., Ross, D.A., Lim, J., et al.: Adaptive discriminative generative model and its applications. In: Neural Information Processing Systems, pp. 801–808 (2004)Google Scholar
- 5.Yang, M., Wu, Y.: Tracking non-stationary appearances and dynamic feature selection. In: 18th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1059–1066. IEEE Computer Society, San Diego (2005)Google Scholar
- 7.Tang, F., Brennan, S., Zhao, Q., et al.: Co-tracking using semi-supervised support vector machines. In: 9th IEEE International Conference on Computer Vision, pp. 1–8. IEEE (2003)Google Scholar
- 8.Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: 13th International Conference on Neural Information Processing Systems, vol. 1, pp. 388–394. MIT Press, Denver (2000)Google Scholar
- 10.Kalal, Z., Matas, J., Mikolajczyk, K.: P-N learning: bootstrapping binary classifiers by structural constraints. In: 23rd IEEE Conference on Computer Vision and Pattern Recognition, vol. 238, pp. 49–56. IEEE Computer Society, San Francisco (2010)Google Scholar