The following is a set of baseline results from different methods. These results were obtained without using the training set. Only the first frame of the each testing sequence was used to estimate key parameters if needed. Please refer to [1] for details. Please refer to the contest page for related codes.
| False-color/Hyperspectral videos | Color videos | ||||
|---|---|---|---|---|---|
| Tracker | DP@20pixels | AUC | DP@20pixels | AUC | |
| MHT [1] | 0.608 | 0.898 | / | / | |
| BACF [2] | 0.581 | 0.861 | 0.549 | 0.775 | |
| SRDCF [3] | 0.535 | 0.790 | 0.510 | 0.754 | |
| fDSST [4] | 0.466 | 0.739 | 0.493 | 0.707 | |
| KCF [5] | 0.385 | 0.629 | 0.416 | 0.647 | |
| DeepSRDCF [6] | 0.575 | 0.880 | 0.587 | 0.835 | |
| C-COT [7] | 0.559 | 0.869 | 0.616 | 0.884 | |
| CFNet [8] | 0.511 | 0.809 | 0.581 | 0.851 | |
| ECO [9] | 0.492 | 0.782 | 0.588 | 0.849 | |
[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.