Abstract
We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis (PCA) subspace, and then we employ an L 1 regularization to restrict the sparsity of the residual term, an L 2 regularization term to restrict the sparsity of the representation coefficients, and an L 2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.
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References
Wang D, Yang J, Yang L and Ge H, Journal of Optoelectronics ·Laser 27, 1066 (2016). (in Chinese)
Li C, Shi J, Xu L and Wei L, Journal of Optoelectronics ·Laser 26, 1381 (2015). (in Chinese)
Yang H, Wu X, He B and Zhu M, Journal of Optoelectronics ·Laser 26, 170 (2015). (in Chinese)
Bai T. and Li Y., IEEE Trans. on Industrial Electronics 10, 538 (2014).
Ross D. A., Lim J., Lin R. S. and Yang M. H., International Journal of Computer Vision 77, 125 (2008).
Turk M. A. and Pentland A. P., J. Cogn. Neurosci. 3, 71 (2001).
Shirazi S., Harandi M., Lovell B.C. and Sanderson C., Object Tracking via Non-euclidean Geometry: A Grassmann Approach, Proc. IEEE Winter Conf. on Applications of Comput. Vision (WACV), 901 (2014).
Kumar B.S., Swamy M.N.S. and Ahmad M.O., Weighted Residual Minimization in PCA Subspace for Visual Tracking, IEEE International Symposium on Circuits and Systems, 986 (2016).
Wright J., Yang A., Ganesh A., Sastry S. and Ma Y., IEEE Transactions on Pattern Analysis & Machine Intelligence 31, 210 (2008).
Yang J., Wright J., Huang T. S. and Ma Y., IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 19, 2861 (2010).
Cevher V., Sankaranarayanan A., Duarte M. F., Reddy D., Baraniuk R. G. and Chellappa R., Compressive Sensing for Background Subtraction, European Conference on Computer Vision, Springer Berlin Heidelberg, 155 (2008).
Mei X., Ling H. B., Wu Y., Blasch E. and Bai L., Minimum Error Bounded Efficient L1 Tracker with Occlusion Detection, The 24th IEEE Conference on Computer Vision and Pattern Recognition, 1257 (2011).
Mei X. and Ling H. B., Robust Visual Tracking using l1 Minimization, IEEE International Conference on Computer Vision, 1436 (2009).
Bao C. L., Wu Y., Ling H. B. and Ji H., Real Time Robust L1 Tracker using Accelerated Proximal Gradient Approach, IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 1830 (2012).
Zhuang B.H., Lu H.C., Xiao Z.Y. and Wang D., IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 23, 1872 (2014).
Wang D., Lu H.C. and Yang M.H., Least Soft-threshold Squares Tracking, IEEE Computer Vision and Pattern Recognition, 2371 (2013).
Xiao Z.Y., Lu H.C. and Wang D., IEEE Transactions on Circuits & Systems for Video Technology 24, 1301 (2014).
Wu Y., Lim J. and Yang M.H., IEEE Transactions on Pattern Analysis & Machine Intelligence 37, 1834 (2015).
Zhong W., Lu H.C. and Yang M.H., Robust Object Tracking via Sparsity-based Collaborative Model, IEEE Computer Vision and Pattern Recognition, 1838 (2012).
Kalal Z., Mikolajczyk K. and Matas J.R., IEEE Transactions on Pattern Analysis & Machine Intelligence 34, 1409 (2012).
Zhang T., Ghanem B. and Liu S., International Journal of Computer Vision 101, 367 (2013).
Everingham M., Van Gool L., Williams C.K., Winn J. and Zisserman A., International Journal of Computer Vision 88, 303 (2010).
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This work has been supported by the National Natural Science Foundation of China (No.61401425).
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Chen, Db., Zhu, M. & Wang, Hl. Visual tracking based on the sparse representation of the PCA subspace. Optoelectron. Lett. 13, 392–396 (2017). https://doi.org/10.1007/s11801-017-7080-z
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DOI: https://doi.org/10.1007/s11801-017-7080-z