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A selection of RGB-T object tracking papers and their performance on various benchmarks.

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RGB-T-Tracking-Papers-and-Results

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

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