Abstract
In recent years, the correlation filter based algorithms show impressive performance for visual tracking. However, the object representations (i.e., feature descriptors) are still not robust. In addition, the models in existing correlation filter based algorithms may be updated by using corrupted samples when the tracking targets are occluded, thus leading to the drifting problem. In this paper, we present a weighted foreground appearance feature descriptor which effectively characterizes the appearance of objects. Moreover, we propose an adaptive model updating strategy to mitigate the problem that the models are updated by using corrupted samples. Our works are based on a recently proposed correlation filter based algorithm, i.e., Staple. By effectively combining the proposed feature descriptor with the adaptively updated Staple framework, the proposed algorithm is highly robust and it can achieve promising performance under complex conditions, such as deformation, rotation and scale variation. Experimental results on the OTB-50 and OTB-100 datasets demonstrate the effectiveness of the proposed tracking algorithm, compared with several other state-of-the-art algorithms.
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References
Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.H.S.: Staple: complementary learners for real-time tracking. In: CVPR, pp. 1401–1409 (2016)
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: CVPR, pp. 2544–2550 (2010)
Casasent, D., Patnaik, R.: Analysis of kernel distortion-invariant filters. In: SPIE, p. 67640Y (2007)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Danelljan, M., Häger, G., Khan, F.S., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: ICCV, pp. 4310–4318 (2015)
Danelljan, M., Häger, G., Shahbaz Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: BMVC (2014)
Gray, R.M.: Toeplitz and Circulant Matrices: A Review. Now Publishers Inc., Norwell (2006)
Hare, S., Saffari, A., Torr, P.H.: Struck: structured output tracking with kernels. In: ICCV, pp. 263–270 (2011)
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
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. PAMI 37(3), 583–596 (2014)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. PAMI 34(7), 1409–1422 (2012)
Liu, S., Zhang, T., Cao, X., Xu, C.: Structural correlation filter for robust visual tracking. In: CVPR, pp. 4312–4320 (2016)
Ma, C., Huang, J.B., Yang, X., Yang, M.H.: Hierarchical convolutional features for visual tracking. In: ICCV, pp. 3074–3082 (2015)
Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking. In: CVPR, pp. 4293–4302 (2016)
Wang, N., Shi, J., Yeung, D.Y., Jia, J.: Understanding and diagnosing visual tracking systems. In: ICCV, pp. 3101–3109 (2015)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: CVPR, pp. 2411–2418 (2013)
Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. IEEE Trans. PAMI 37(9), 1834–1848 (2015)
Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254–265. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_18
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grants U1605252, 61472334, 61571379 and 61370124, and by the Natural Science Foundation of Fujian Province of China under Grant 2017J01127.
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Qie, C., Wang, H., Yan, Y., Guo, G., Zheng, J. (2018). Adaptive Correlation Filter Tracking with Weighted Foreground Representation. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_22
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DOI: https://doi.org/10.1007/978-3-319-77380-3_22
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