[1] Chen W M, Hong R, Gai S Y et al. Three-dimensional multi-object tracking based on feature fusion and similarity estimation network[J]. Acta Optica Sinica, 42, 1615001(2022).

[2] Yang J, Ma L. Thermal infrared object tracking method based on positional perception[J]. Laser & Optoelectronics Progress, 60, 1210007(2023).

[3] Liang Z Q, Wang J S, Xiao G et al. FAANet: feature-aligned attention network for real-time multiple object tracking in UAV videos[J]. Chinese Optics Letters, 20, 081101(2022).

[4] Cai W, Wang D L, Feng W et al. High-resolution acoustic tracking method for underwater target using laser-based sensor[J]. Chinese Journal of Lasers, 49, 1810004(2022).

[5] Zhao D L, Zhang Y A, He G H et al. Brightness enhancement algorithm for infrared digital holographic image through smoke[J]. Chinese Journal of Lasers, 50, 1809001(2023).

[6] Wang F T, Wang W Q, Liu L et al. Siamese transformer RGBT tracking[J]. Applied Intelligence, 53, 24709-24723(2023).

[7] Li M Y, Zhang P, Yan M et al. Dynamic feature-memory transformer network for RGBT tracking[J]. IEEE Sensors Journal, 23, 19692-19703(2023).

[8] Conaire C Ó, O'Connor N E, Smeaton A. Thermo-visual feature fusion for object tracking using multiple spatiogram trackers[J]. Machine Vision and Applications, 19, 483-494(2008).

[9] Li C L, Sun X, Wang X et al. Grayscale-thermal object tracking via multitask Laplacian sparse representation[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 673-681(2017).

[10] Li C L, Cheng H, Hu S Y et al. Learning collaborative sparse representation for grayscale-thermal tracking[J]. IEEE Transactions on Image Processing, 25, 5743-5756(2016).

[11] Li C L, Liang X Y, Lu Y J et al. RGB-T object tracking: benchmark and baseline[J]. Pattern Recognition, 96, 106977(2019).

[12] Li C L, Xue W L, Jia Y Q et al. LasHeR: a large-scale high-diversity benchmark for RGBT tracking[J]. IEEE Transactions on Image Processing, 31, 392-404(2022).

[13] Zhang P Y, Zhao J, Wang D et al. Visible-thermal UAV tracking: a large-scale benchmark and new baseline[C], 8876-8885(2022).

[14] Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking[C], 4293-4302(2016).

[15] Li C L, Lu A D, Zheng A H et al. Multi-adapter RGBT tracking[C], 2262-2270(2019).

[16] Li C L, Liu L, Lu A D, Vedaldi A, Bischof H, Brox T et al. Challenge-aware RGBT tracking[M]. Computer vision-ECCV 2020, 12367, 222-237(2020).

[17] Xiao Y, Yang M M, Li C L et al. Attribute-based progressive fusion network for RGBT tracking[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2831-2838(2022).

[18] Zhang X C, Ye P, Peng S Y et al. SiamFT: an RGB-infrared fusion tracking method via fully convolutional Siamese networks[J]. IEEE Access, 7, 122122-122133(2019).

[19] Zhang T L, Liu X R, Zhang Q et al. SiamCDA: complementarity- and distractor-aware RGB-T tracking based on Siamese network[J]. IEEE Transactions on Circuits and Systems for Video Technology, 32, 1403-1417(2022).

[20] Li B, Wu W, Wang Q et al. SiamRPN: evolution of Siamese visual tracking with very deep networks[C], 4277-4286(2019).

[21] Guo C Y, Xiao L. High speed and robust RGB-thermal tracking via dual attentive stream Siamese network[C], 803-806(2022).

[22] Guo D Y, Wang J, Cui Y et al. SiamCAR: Siamese fully convolutional classification and regression for visual tracking[C], 6268-6276(2020).

[23] Bertinetto L, Valmadre J, Henriques J F, Hua G, Jégou H et al. Fully-convolutional Siamese networks for object tracking[M]. Computer vision-ECCV 2016 workshops, 9914, 850-865(2016).

[24] Zhang L G, Ma Z J, Jin M et al. Nonlocal neural network-based moving target tracking method[J]. Laser & Optoelectronics Progress, 60, 0415007(2023).

[25] Chen X, Yan B, Zhu J W et al. Transformer tracking[C], 8122-8131(2021).

[26] Zhang Z P, Peng H W. Deeper and wider Siamese networks for real-time visual tracking[C], 4586-4595(2019).

[27] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[28] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[29] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database[C], 248-255(2009).

[30] Wang C Q, Xu C Y, Cui Z et al. Cross-modal pattern-propagation for RGB-T tracking[C], 7062-7071(2020).

[31] Zhu Y B, Li C L, Tang J et al. Quality-aware feature aggregation network for robust RGBT tracking[J]. IEEE Transactions on Intelligent Vehicles, 6, 121-130(2021).

[32] Zhang P Y, Wang D, Lu H C et al. Learning adaptive attribute-driven representation for real-time RGB-T tracking[J]. International Journal of Computer Vision, 129, 2714-2729(2021).

[33] Tu Z Z, Lin C, Zhao W et al. M5L: multi-modal multi-margin metric learning for RGBT tracking[J]. IEEE Transactions on Image Processing, 31, 85-98(2022).

[34] Peng J C, Zhao H T, Hu Z W. Dynamic fusion network for RGBT tracking[J]. IEEE Transactions on Intelligent Transportation Systems, 24, 3822-3832(2023).

[35] Tang Z Y, Xu T Y, Li H et al. Exploring fusion strategies for accurate RGBT visual object tracking[J]. Information Fusion, 99, 101881(2023).

[36] Zhu Y B, Li C L, Luo B et al. Dense feature aggregation and pruning for RGBT tracking[C], 465-472(2019).

[37] Mei J T, Zhou D M, Cao J D et al. HDINet: hierarchical dual-sensor interaction network for RGBT tracking[J]. IEEE Sensors Journal, 21, 16915-16926(2021).

[38] Lu A D, Li C L, Yan Y Q et al. RGBT tracking via multi-adapter network with hierarchical divergence loss[J]. IEEE Transactions on Image Processing, 30, 5613-5625(2021).

[39] Lu A D, Qian C, Li C L et al. Duality-gated mutual condition network for RGBT tracking[EB/OL]. https:∥ieeexplore.ieee.org/document/9737634

[40] Li C L, Zhao N, Lu Y J et al. Weighted sparse representation regularized graph learning for RGB-T object tracking[C], 1856-1864(2017).