[1] Mao N, Yang D D, Li Y et al. Spatial regularization correlation filtering tracking via deformable diversity similarity[J]. Acta Optica Sinica, 39, 0415002(2019).

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

[3] Li B, Yan J J, Wu W et al. High performance visual tracking with Siamese region proposal network[C], 8971-8980(2018).

[4] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

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

[6] Danelljan M, Bhat G, Khan F S et al. ATOM: accurate tracking by overlap maximization[C], 4655-4664(2019).

[7] Jiang B, Luo R, Mao J et al. Acquisition of localization confidence for accurate object detection[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11218, 816-832(2018).

[8] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[9] Li X, Wang W H, Hu X L et al. Selective kernel networks[C], 510-519(2019).

[10] He A F, Luo C, Tian X M et al. A twofold Siamese network for real-time object tracking[C], 4834-4843(2018).

[11] Yu Y C, Xiong Y L, Huang W L et al. Deformable Siamese attention networks for visual object tracking[C], 6727-6736(2020).

[12] Cao Y, Xu J R, Lin S et al. GCNet: non-local networks meet squeeze-excitation networks and beyond[C], 1971-1980(2019).

[13] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 3-19(2018).

[14] Fan H, Lin L T, Yang F et al. LaSOT: a high-quality benchmark for large-scale single object tracking[C], 5369-5378(2019).

[15] Müller M, Bibi A, Giancola S et al. TrackingNet: a large-scale dataset and benchmark for object tracking in the wild[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11205, 310-327(2018).

[16] Huang L H, Zhao X, Huang K Q. GOT-10k: a large high-diversity benchmark for generic object tracking in the wild[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 1562-1577(2021).

[17] Lin T Y, Maire M, Belongie S et al. Microsoft COCO: common objects in context[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014, 8693, 740-755(2014).

[18] Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1834-1848(2015).

[19] Kristan M, Leonardis A, Matas J et al. The sixth visual object tracking VOT2018 challenge results[M]. Leal-Taixé L, Roth S. Computer vision-ECCV 2018 workshops, 11129, 3-53(2018).

[20] Danelljan M, Häger G, Khan F S et al. Convolutional features for correlation filter based visual tracking[C], 621-629(2015).

[21] Danelljan M, Häger G, Khan F S et al. Learning spatially regularized correlation filters for visual tracking[C], 4310-4318(2015).

[22] Song Y B, Ma C, Wu X H et al. VITAL: visual tracking via adversarial learning[C], 8990-8999(2018).

[23] Danelljan M, Bhat G, Khan F S et al. ECO: efficient convolution operators for tracking[C], 6931-6939(2017).

[24] Wang G T, Luo C, Xiong Z W et al. SPM-tracker: series-parallel matching for real-time visual object tracking[C], 3638-3647(2019).

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

[26] Wang Q, Zhang L, Bertinetto L et al. Fast online object tracking and segmentation: a unifying approach[C], 1328-1338(2019).

[27] Lukežič A, Matas J, Kristan M. D3S: a discriminative single shot segmentation tracker[C], 7131-7140(2020).

[28] Jung I, Son J, Baek M et al. Real-time mdnet[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11208, 89-104(2018).