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. The results will be released later. Stay tuned.
| False-color/Hyperspectral videos | ||
|---|---|---|
| Tracker | AUC | DP@20pixels |
| MHT [1] | 0.4528 | 0.7168 |
| SiamBAN [2] | 0.532 | 0.756 |
| SiamCAR [3] | 0.554 | 0.768 |
| SiamGAT [4] | 0.561 | 0.770 |
| STARK [5] | 0.557 | 0.762 |
| TranST [6] | 0.569 | 0.777 |
[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. code_2 (this version can adapt to arbitary bands)Results
[2] Chen Z, Zhong B, Li G, et al. Siamese box adaptive network for visual tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 6668-6677. code Results
[3] Guo D, Wang J, Cui Y, et al. SiamCAR: Siamese fully convolutional classification and regression for visual tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 6269-6277. code Results
[4] Guo D, Shao Y, Cui Y, et al. Graph attention tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 9543-9552. code Results
[5] Yan B, Peng H, Fu J, et al. Learning spatio-temporal transformer for visual tracking[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 10448-10457. code Results
[6] Chen X, Yan B, Zhu J, et al. Transformer tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 8126-8135. code Results