[1] F. Xiong, J. Zhou and Y. Qian, "Material Based Object Tracking in Hyperspectral Videos," IEEE Trans. Image Process., vol. 29, no. 1, pp. 3719-3733, 2020.
[2] H. K. Galoogahi, A. Fagg, and S. Lucey, “Learning background-aware correlation filters for visual tracking,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 1144–1152.
[3] M. Danelljan, G. Hger, F. S. Khan, and M. Felsberg, “Learning spatially regularized correlation filters for visual tracking,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2015, pp. 4310–4318.
[4] M. Danelljan, G. Hger, F. S. Khan, and M. Felsberg, “Discriminative scale space tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 8, pp. 1561–1575, 2017.
[5] J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, “High-speed tracking with kernelized correlation filters,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 583–596, 2015.
[6] M. Danelljan, G. Hger, F. S. Khan, and M. Felsberg, “Convolutional features for correlation filter based visual tracking,” in Proc. IEEE Int. Conf. Comput. Vis. Workshop (ICCVW), 2015, pp. 621–629.
[7] M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg, “Beyond correlation filters: Learning continuous convolution operators for visual tracking,” in Proc. Eur. Conf. Comput. Vis. (ECCV), 2016, pp. 472–488.
[8] M. Danelljan, G. Bhat, F. S. Khan, and M. Felsberg, “ECO: Efficient convolution operators for tracking.” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 6638–6646.
[9] J. Valmadre, L. Bertinetto, J. Henriques, A. Vedaldi, and P. H. S. Torr, “End-to-end representation learning for correlation filter based tracking,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 2805–2813.