Abstract
Visual tracking is a fundamental computer vision task with a wide range of applications. Kernelized Correlation Filter (KCF) is an excellent algorithm with high tracking speed. However, the target tracking scale in the KCF algorithm is a fixed value which might cause tracking failure or target drifting problem when the target scale changes significantly. In this paper, we present an adaptive multi-scale tracking algorithm based on the KCF algorithm by estimating the scale of the target. Our method builds upon the correlation filter with a Gaussian kernel and reasonable prediction of the target size. In order to verify the effectiveness of the proposed algorithm, 9 sets of complex video sequences of a commonly used tracking benchmark were selected and the results were compared with other tracking methods (KCF, CSK, CT, TLD, Struck, CNN-SVM and MDNet). The results show that the proposed method has high accuracy. The method in this paper has strong robustness in the complex scenes with challenges of scale variation, illumination variation, occlusion, in-plane rotation, out-of plane rotation and deformation.
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References
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)
Vojir, T., Noskova, J., Matas, J.: Robust scale-adaptive mean-shift for tracking. Pattern Recognit. Lett. 49(C), 250–258 (2013)
Smeulders, A.W.M., et al.: Visual tracking: an experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2014)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409 (2012)
Zhang, K., et al.: Fast Tracking via spatio-temporal context learning. In: Computer Science (2013)
Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33712-3_62
Henriques, J.F., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Patt. Anal. Mach. Intell. 37(3), 583 (2015)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_50
Bolme, D.S., et al.: Visual object tracking using adaptive correlation filters. In: Computer Vision and Pattern Recognition IEEE, pp. 2544–2550 (2010)
Rifkin, R., Yeo, G., Poggio, T.: Regularized least-squares classification. Nato Sci. Ser. Sub Ser. III 190, 131–154 (2003)
Scholkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Upper Saddle River (2008)
Dollar, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532–1545 (2014)
Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)
Babenko, B., Yang, M.-H., Belongie, S.: Robust object tracking with online multiple instance learning. TPAMI 33(8), 1619–1632 (2011)
Hare, S., Saffari, A., Torr, P.H.S.: Struck: structured output tracking with kernels. In: IEEE International Conference on Computer Vision, pp. 263–270. IEEE (2012)
Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network. In: ICML (2015)
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR (2016)
Acknowledgement
This work was supported in Science and Technology Commission of Shanghai Municipality (STCSM, Grant Nos. 15DZ1207403, 17DZ1205602).
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Xu, Q., Yang, H. (2018). An Adaptive Multi-scale Tracking Method Based on Kernelized Correlation Filter. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_39
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DOI: https://doi.org/10.1007/978-981-10-8108-8_39
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