A selection of RGB-TIR object tracking papers and their performance on various benchmarks.
| Tracker | Year | GTOT50-PR/SR | RGBT210-PR/SR | RGBT234-PR/SR | LasHeR-PR/SR | Code | Paper |
|---|---|---|---|---|---|---|---|
| [1] | 2018 | 0.852/0.626 | -/- | -/- | -/- | [[Code]] | [Paper] |
| mfDiMP [2] | 2019 | -/- | 0.786 /0.555 | 0.785/0.559 | 0.447/0.344 | [Code] | [Paper] |
| MANet [3] | 2019 | 0.894/0.724 | -/- | 0.777/0.539 | 0.457/0.33 | [Code] | [Paper] |
| DAPNet [4] | 2019 | 0.882/0.707 | -/- | 0.766/0.537 | 0.431/0.314 | [[Code]] | [Paper] |
| DAFNet [5] | 2019 | 0.891/0.712 | -/- | 0.796/0.544 | 0.449/0.311 | [Code] | [Paper] |
| [6] | 2019 | 0.843/0.677 | -/- | 0.787/0.545 | -/- | [[Code]] | [Paper] |
| [7] | 2019 | -/- | -/- | 0.610/0.428 | -/- | [[Code]] | [Paper] |
| SiamFT [8] | 2019 | 0.826/0.700 | -/- | 0.688/0.486 | -/- | [[Code]] | [Paper] |
| MaCNet [9] | 2020 | 0.880/0.714 | -/- | 0.790/0.554 | 0.483/0.352 | [[Code]] | [Paper] |
| DMCNet [10] | 2022 | 0.909/0.733 | 0.797/0.555 | 0.839/0.593 | 0.491/0.357 | [[Code]] | [Paper] |
| CMPP [11] | 2020 | 0.926/0.738 | -/- | 0.823/0.575 | -/- | [[Code]] | [Paper] |
| CAT [12] | 2020 | 0.889/0.717 | 0.792/0.533 | 0.804/0.561 | 0.451/0.317 | [Code] | [Paper] |
| DSiamMFT [13] | 2020 | -/- | 0.642/0.432 | -/- | -/- | [[Code]] | [Paper] |
| JMMAC [14] | 2021 | 0.902/0.732 | -/- | 0.790/0.573 | -/- | [[Code]] | [Paper] |
| MANet++ [15] | 2021 | 0.901/0.723 | -/- | 0.800/0.554 | 0.467/0.317 | [[Code]] | [Paper] |
| CBPNet [16] | 2022 | 0.885/0.716 | -/- | 0.794/0.541 | -/- | [[Code]] | [Paper] |
| TFNet [17] | 2022 | 0.886/0.729 | 0.777/0.529 | 0.806/0.560 | -/- | [[Code]] | [Paper] |
| FANet [18] | 2018 | 0.891/0.728 | -/- | 0.787/0.553 | 0.442/0.309 | [[Code]] | [Paper] |
| ADRNet [19] | 2021 | 0.904/0.739 | -/- | 0.809/0.571 | -/- | [Code] | [Paper] |
| M 5 L [20] | 2022 | 0.896/0.710 | -/- | 0.795/0.542 | -/- | [[Code]] | [Paper] |
| SiamCDA [21] | 2022 | 0.877/0.732 | -/- | 0.760/0.569 | -/- | [[Code]] | [Paper] |
| DuSiamRT [22] | 2022 | 0.766/0.628 | -/- | 0.567/0.384 | -/- | [[Code]] | [Paper] |
| APFNet [23] | 2022 | 0.905/0.739 | -/- | 0.827/0.579 | 0.500/0.362 | [Code] | [Paper] |
| MFGNet [24] | 2022 | 0.889/0.707 | 0.749/0.494 | 0.783/0.535 | -/- | [Code] | [Paper] |
| ViPT [25] | 2023 | -/- | -/- | 0.835/0.617 | 0.651/0.525 | [Code] | [Paper] |
| ProTrack [26] | 2023 | -/- | -/- | 0.786/0.587 | 0.509/0.421 | [[Code]] | [Paper] |
| EANet [27] | 2023 | -/- | -/- | 0.835/0.584 | 0.506/0.367 | [Code] | [Paper] |
REFERENCES
[1] C. Li, X. Wu, N. Zhao, X. Cao, and J. Tang, “Fusing two-stream convolutional neural networks for rgb-t object tracking,” Neurocomputing, vol. 281, pp. 78–85, 2018.
[2] L. Zhang, M. Danelljan, A. Gonzalez-Garcia, J. van de Weijer, and F. S. Khan, “Multi-modal fusion for end-to-end rgb-t tracking,” pp. 2252–2261, IEEE, 10 2019.
[3] C. Li, A. Lu, A. Zheng, Z. Tu, and J. Tang, “Multi-adapter rgbt tracking,” pp. 2262–2270, 2019.
[4] Y. Zhu, C. Li, B. Luo, J. Tang, and X. Wang, “Dense feature aggregation and pruning for rgbt tracking,” pp. 465–472, 2019.
[5] Y. Gao, C. Li, Y. Zhu, J. Tang, T. He, and F. Wang, “Deep adaptive fusion network for high performance rgbt tracking,” pp. 91–99, Institute of Electrical and Electronics Engineers Inc., 10 2019.
[6] R. Yang, Y. Zhu, X. Wang, C. Li, and J. Tang, “Learning target-oriented dual attention for robust rgb-t tracking,” 8 2019.
[7] X. Zhang, P. Ye, D. Qiao, J. Zhao, S. Peng, and G. Xiao, “Object fusion tracking based on visible and infrared images using fully convolutional siamese networks,” pp. 1–8, IEEE, 7 2019.
[8] X. Zhang, P. Ye, S. Peng, J. Liu, K. Gong, and G. Xiao, “Siamft: An rgb-infrared fusion tracking method via fully convolutional siamese networks,” IEEE Access, vol. 7, pp. 122122–122133, 2019.
[9] H. Zhang, L. Zhang, L. Zhuo, and J. Zhang, “Object tracking in rgb-t videos using modal-aware attention network and competitive learning,” Sensors (Switzerland), vol. 20, 1 2020.
[10] A. Lu, C. Qian, C. Li, J. Tang, and L. Wang, “Duality-gated mutual condition network for rgbt tracking,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14, 2022.
[11] C. Wang, C. Xu, Z. Cui, L. Zhou, T. Zhang, X. Zhang, and J. Yang, “Cross-modal pattern-propagation for rgb-t tracking,” pp. 7062–7071, IEEE Computer Society, 2020.
[12] C. Li, L. Liu, A. Lu, Q. Ji, and J. Tang, “Challenge-aware rgbt tracking,” 7 2020.
[13] X. Zhang, P. Ye, S. Peng, J. Liu, and G. Xiao, “Dsiammft: An rgb-t fusion tracking method via dynamic siamese networks using multi-layer feature fusion,” Signal Processing: Image Communication, vol. 84, 5 2020.
[14] P. Zhang, J. Zhao, C. Bo, D. Wang, H. Lu, and X. Yang, “Jointly modeling motion and appearance cues for robust rgb-t tracking,” IEEE Transactions on Image Processing, vol. 30, pp. 3335–3347, 2021
[15] A. Lu, C. Li, Y. Yan, J. Tang, and B. Luo, “Rgbt tracking via multi-adapter network with hierarchical divergence loss,” IEEE Transactions on Image Processing, vol. 30, pp. 5613–5625,2021.
[16] Q. Xu, Y. Mei, J. Liu, and C. Li, “Multimodal cross-layer bilinear pooling for rgbt tracking,” IEEE Transactions on Multimedia, vol. 24, pp. 567–580, 2022.
[17] Y. Zhu, C. Li, J. Tang, B. Luo, and L. Wang, “Rgbt tracking by trident fusion network,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, pp. 579–592, 2 2022.
[18] Y. Zhu, C. Li, B. Luo, and J. Tang, “Fanet: Quality-aware feature aggregation network for robust rgb-t tracking,” 11 2018.
[19] P. Zhang, D. Wang, H. Lu, and X. Yang, “Learning adaptive attribute-driven representation for real-time rgb-t tracking,” International Journal of Computer Vision, vol. 129, pp. 2714–2729, 9 2021.
[20] Z. Tu, C. Lin, W. Zhao, C. Li, and J. Tang, “M 5 l: Multi-modal multi-margin metric learning for rgbt tracking,” IEEE Transactions on Image Processing, vol. 31, pp. 85–98, 2022.
[21] T. Zhang, X. Liu, Q. Zhang, and J. Han, “Siamcda: Complementarity- and distractor-aware rgb-t tracking based on siamese network,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, pp. 1403–1417, 3 2022.
[22] C. Guo, D. Yang, C. Li, and P. Song, “Dual siamese network for rgbt tracking via fusing predicted position maps,” The Visual Computer, vol. 38, pp. 2555–2567, 7 2022.
[23] Y. Xiao, M. Yang, C. Li, L. Liu, and J. Tang, “Attribute-based progressive fusion network for rgbt tracking,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2831–2838,6 2022.
[24] Xiao Wang, Xiujun Shu, Shiliang Zhang, Bo Jiang, Yaowei Wang, Yonghong Tian, Feng Wu, "Dynamic Modality-Aware Filter Generation for RGB-T Tracking", Accepted by IEEE Transactions on Multimedia (TMM), 2022.
[25] J. Zhu, S. Lai, X. Chen, D. Wang, and H. Lu, “Visual Prompt Multi-Modal Tracking,” arXiv.org, Mar. 20, 2023. https://arxiv.org/abs/2303.10826 (accessed Apr. 24, 2023).
[26] J. Yang, Z. Li, F. Zheng, A. Leonardis, and J. Song, “Prompting for Multi-Modal Tracking,” arXiv.org, Jul. 29, 2022. https://arxiv.org/abs/2207.14571 (accessed Apr. 25, 2023).
[27] Abbas Türkoğlu and Erdem Akagündüz "EANet: enhanced attribute-based RGBT tracker network", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 1307218 (3 April 2024); https://doi.org/10.1117/12.3023347