运算资源受限环境下的目标跟踪算法综述

doi:10.3778/j.issn.1002-8331.2107-0337

摘要/Abstract

摘要：

自深度神经网络出现以来，目标跟踪技术领域的发展也取得了长足的进步。当前目标跟踪领域的研究大多数都集中在算力充沛的计算环境下提升算法的准确度与效率，在运算资源受限环境下的目标跟踪算法研究相对较少。因此，开发在运算资源受限环境下仍然有效的跟踪网络至关重要。对近年目标跟踪技术所取得的进展与设计理念进行了系统性的梳理，并总结其在适配运算资源受限环境下的改进。介绍了目标跟踪任务的整体工作流程，并根据各跟踪方法的侧重点做出归纳。总结了目标跟踪任务中已有的数据集与可用于模型评估的评价指标，以便于研究人员根据实际任务的需求来确定具体使用的数据集；结合现有的工作，发掘未来的研究方向。

关键词: 目标跟踪, 深度学习, 资源受限

Abstract:

Since the emergence of Deep Neural Network（DNN）, the development of target tracking technology has also made great progress. Most of the current research in the field of target tracking is focused on improving the accuracy and efficiency of the algorithm in a computing environment with sufficient computing power. There are relatively few target tracking algorithms in a resource-constrained environment. Therefore, it is essential to develop a tracking network that is still effective in resource-constrained environments. This article systematically sorts out the progress and design concepts of target tracking technology in resource-constrained environments in recent years. This paper introduces the overall workflow of the target tracking task, and makes a summary based on the focus of each tracking method. This paper also summarizes the existing data set in the target tracking task and the evaluation indicators that can be used for model evaluation to facilitate research. The personnel determine the specific data set to be used according to the needs of the actual task. Existing work is combined to explore future research directions.

Key words: object tracking, deep learning, restricted resource

武哲纬，周世杰，刘启和. 运算资源受限环境下的目标跟踪算法综述[J]. 计算机工程与应用, 2021, 57(21): 24-40.

WU Zhewei, ZHOU Shijie, LIU Qihe. Review on Object Tracking Methods for Restricted Computing Resources[J]. Computer Engineering and Applications, 2021, 57(21): 24-40.

参考文献

[1] CHANG M F，LAMBERT J，SANGKLOY P，et al.Argoverse：3d tracking and forecasting with rich maps[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：8748-8757.
[2] GIRAO P，ASVADI A，PEIXOTO P，et al.3d object tracking in driving environment：a short review and a benchmark dataset[C]//2016 IEEE 19th International Conference on Intelligent Transportation Systems（ITSC），2016：7-12.
[3] ROBIN C，LACROIX S J A R.Multi-robot target detection and tracking：taxonomy and survey[J].Proceedings of SPIE the International Society for Optical Engineering，2016， 40（4）：729-760.
[4] YOU S，ZHU H，LI M，et al.A review of visual trackers and analysis of its application to mobile robot[J].arXiv：1910.09761，2019.
[5] HAO J，ZHOU Y，ZHANG G，et al.A review of target tracking algorithm based on uav[C]//2018 IEEE International Conference on Cyborg and Bionic Systems（CBS），2018：328-333.
[6] COMANICIU D，RAMESH V，MEER P.Kernel-based object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2003，25（5）：564-577.
[7] KIM D H，KIM H K，LEE S J，et al.Kernel-based structural binary pattern tracking[J].IEEE Transactions on Circuits and Systems for Video Technology，2014，24（8）：1288-1300.
[8] BOUGUET J Y.Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm[J].Intel Corporation，2001，5（1/10）：4.
[9] DAN L，DAI-HONG J，RONG B，et al.Moving object tracking method based on improved lucas-kanade sparse optical flow algorithm[C]//2017 International Smart Cities Conference（ISC2），2017：1-5.
[10] BOLME D S，BEVERIDGE J R，DRAPER B A，et al.Visual object tracking using adaptive correlation filters[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition，2010：2544-2550.
[11] DANELLJAN M，H?GER G，KHAN F S，et al.Discriminative scale space tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2016，39（8）：1561-1575.
[12] HENRIQUES J F，CASEIRO R，MARTINS P，et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2014，37（3）：583-596.
[13] BRADSKI G R.Computer vision face tracking for use in a perceptual user interface[C]//Proceedings of IEEE Workshop Applications of Computer Vision，1998：214-219.
[14] DANELLJAN M，HAGER G，SHAHBAZ KHAN F，et al.Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision，2015：4310-4318.
[15] KIANI GALOOGAHI H，SIM T，LUCEY S.Correlation filters with limited boundaries[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：4630-4638.
[16] LOWE D G.Object recognition from local scale-invariant features[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision，1999：1150-1157.
[17] BAY H，TUYTELAARS T，VAN GOOL L.Surf：speeded up robust features[C]//European Conference on Computer Vision，2006：404-417.
[18] DALAL N，TRIGGS B.Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition（CVPR’05），2005：886-893.
[19] VAN DE WEIJER J，SCHMID C，VERBEEK J，et al.Learning color names for real-world applications[J].IEEE Transactions on Image Processing，2009，18（7）：1512-1523.
[20] BERTINETTO L，VALMADRE J，HENRIQUES J F，et al.Fully-convolutional siamese networks for object tracking[C]//European Conference on Computer Vision，2016：850-865.
[21] WANG Q，ZHANG L，BERTINETTO L，et al.Fast online object tracking and segmentation：a unifying approach[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：1328-1338.
[22] LI B，YAN J，WU W，et al.High performance visual tracking with siamese region proposal network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：8971-8980.
[23] SONG Y，MA C，WU X，et al.Vital：visual tracking via adversarial learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：8990-8999.
[24] NAM H，BAEK M，HAN B.Modeling and propagating cnns in a tree structure for visual tracking[J].arXiv：1608.07242，2016.
[25] NAM H，HAN B.Learning multi-domain convolutional neural networks for visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：4293-4302.
[26] LI X，MA C，WU B，et al.Target-aware deep tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：1369-1378.
[27] DANELLJAN M，ROBINSON A，KHAN F S，et al.Beyond correlation filters：learning continuous convolution operators for visual tracking[C]//European Conference on Computer Vision，2016：472-488.
[28] BHAT G，JOHNANDER J，DANELLJAN M，et al.Unveiling the power of deep tracking[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：483-498.
[29] DANELLJAN M，HAGER G，SHAHBAZ KHAN F，et al.Convolutional features for correlation filter based visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2015：58-66.
[30] DANELLJAN M，BHAT G，SHAHBAZ KHAN F，et al.Eco：efficient convolution operators for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：6638-6646.
[31] LUCAS B D，KANADE T.An iterative image registration technique with an application to stereo vision[C]//Proceedings of the International Joint Conference on Artificial Intelligence，1981：674-679.
[32] HENRIQUES J F，CASEIRO R，MARTINS P，et al.Exploiting the circulant structure of tracking-by-detection with kernels[C]//European Conference on Computer Vision，2012：702-715.
[33] LI Y，ZHU J.A scale adaptive kernel correlation filter tracker with feature integration[C]//European Conference on Computer Vision，2014：254-265.
[34] TAO R，GAVVES E，SMEULDERS A W.Siamese instance search for tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：1420-1429.
[35] KRIZHEVSKY A，SUTSKEVER I，HINTON G E.Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems，2012：1097-1105.
[36] SIMONYAN K，ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv：1409. 1556，2014.
[37] HE K，ZHANG X，REN S，et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：770-778.
[38] HELD D，THRUN S，SAVARESE S.Learning to track at 100?FPS with deep regression networks[C]//European Conference on Computer Vision，2016：749-765.
[39] WANG Q，GAO J，XING J，et al.Dcfnet：discriminant correlation filters network for visual tracking[J].arXiv：1704.04057，2017.
[40] SONG Y，MA C，GONG L，et al.Crest：convolutional residual learning for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：2555-2564.
[41] HUANG C，LUCEY S，RAMANAN D.Learning policies for adaptive tracking with deep feature cascades[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：105-114.
[42] WANG Q，TENG Z，XING J，et al.Learning attentions：residual attentional siamese network for high performance online visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：4854-4863.
[43] YANG T，CHAN A B.Learning dynamic memory networks for object tracking[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：152-167.
[44] LI B，WU W，WANG Q，et al.Siamrpn++：evolution of siamese visual tracking with very deep networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：4282-4291.
[45] 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，2019：6269-6277.
[46] WANG G，LUO C，XIONG Z，et al.Spm-tracker：series-parallel matching for real-time visual object tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：3643-3652.
[47] DANELLJAN M，BHAT G，KHAN F S，et al.Atom：accurate tracking by overlap maximization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：4660-4669.
[48] FAN H，LING H.Siamese cascaded region proposal networks for real-time visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：7952-7961.
[49] ZHANG Y，WANG L，QI J，et al.Structured siamese network for real-time visual tracking[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：351-366.
[50] ZHENG L，TANG M，CHEN Y，et al.Learning feature embeddings for discriminant model based tracking[C]//16th European Conference Computer Vision（ECCV 2020），Glasgow，UK，August 23-28，2020.
[51] LIAO B，WANG C，WANG Y，et al.Pg-Net：pixel to global matching network for visual tracking[C]//European Conference on Computer Vision，2020：429-444.
[52] HE A，LUO C，TIAN X，et al.A two fold siamese network for real-time object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：4834-4843.
[53] YU Y，XIONG Y，HUANG W，et al.Deformable siamese attention networks for visual object tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：6728-6737.
[54] GAO J，ZHANG T，XU C.Graph convolutional tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：4649-4659.
[55] CHOI J，KWON J，LEE K M.Deep meta learning for real-time target-aware visual tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：911-920.
[56] YANG T，XU P，HU R，et al.Roam：recurrently optimizing tracking model[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：6718-6727.
[57] HE A，LUO C，TIAN X，et al.Towards a better match in siamese network based visual object tracker[C]//Proceedings of the European Conference on Computer Vision（ECCV） Workshops，2018.
[58] ZHANG L，GONZALEZ-GARCIA A，WEIJER J V D，et al.Learning the model update for siamese trackers[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：4010-4019.
[59] LI P，CHEN B，OUYANG W，et al.Gradnet：gradient-guided network for visual object tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：6162-6171.
[60] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[J].arXiv：1506.01497，2015.
[61] ZHU Z，WANG Q，LI B，et al.Distractor-aware siamese networks for visual object tracking[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：101-117.
[62] YAN B，ZHAO H，WANG D，et al.‘Skimming-Perusal’ tracking：a framework for real-time and robust long-term tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：2385-2393.
[63] XU Y，WANG Z，LI Z，et al.Siamfc++：towards robust and accurate visual tracking with target estimation guidelines[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2020：12549-12556.
[64] ZHANG Z，PENG H，FU J，et al.Ocean：object-aware anchor-free tracking[J].arXiv：2006.10721，2020.
[65] HAN G，DU H，LIU J，et al.Fully conventional anchor-free siamese networks for object tracking[J].IEEE Access，2019，7：123934-123943.
[66] DU F，LIU P，ZHAO W，et al.Correlation-guided attention for corner detection based visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：6836-6845.
[67] DANELLJAN M，GOOL L V，TIMOFTE R.Probabilistic regression for visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：7183-7192.
[68] DONG X，SHEN J，SHAO L，et al.Clnet：a compact latent network for fast adjusting siamese trackers[C]//Proceedings of the 16th European Conference on Computer Vision，2020：378-395.
[69] LUKEZIC A，MATAS J，KRISTAN M.D3s-a discriminative single shot segmentation tracker[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：7133-7142.
[70] DIAO Z.A single target tracking algorithm based on generative adversarial networks[J].arXiv：1912.11967，2019.
[71] WANG X，LI C，LUO B，et al.Sint++：robust visual tracking via adversarial positive instance generation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：4864-4873.
[72] ZHU Z，WU W，ZOU W，et al.End-to-end flow correlation tracking with spatial-temporal attention[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：548-557.
[73] BHAT G，DANELLJAN M，VAN GOOL L，et al.Know your surroundings：exploiting scene information for object tracking[C]//European Conference on Computer Vision，2020：205-221.
[74] FAN H，LING H.Sanet：structure-aware network for visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops，2017：42-49.
[75] TENG Z，XING J，WANG Q，et al.Robust object tracking based on temporal and spatial deep networks[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：1144-1153.
[76] NING G，ZHANG Z，HUANG C，et al.Spatially supervised recurrent convolutional neural networks for visual object tracking[C]//2017 IEEE International Symposium on Circuits and Systems（ISCAS），2017：1-4.
[77] GORDON D，FARHADI A，FOX D.Re3：real-time recurrent regression networks for visual tracking of generic objects[J].IEEE Robotics and Automation Letters，2018，3（2）：788-795.
[78] YANG T，CHAN A B.Recurrent filter learning for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2017：2010-2019.
[79] LIU Y，LI R，CHENG Y，et al.Object tracking using spatio-temporal networks for future prediction location[C]//European Conference on Computer Vision，2020：1-17.
[80] POSSEGGER H，MAUTHNER T，BISCHOF H.In defense of color-based model-free tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2015：2113-2120.
[81] WANG L，OUYANG W，WANG X，et al.Stct：sequentially training convolutional networks for visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：1373-1381.
[82] JUNG I，SON J，BAEK M，et al.Real-time mdnet[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：83-98.
[83] WANG G，LUO C，SUN X，et al.Tracking by instance detection：a meta-learning approach[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：6288-6297.
[84] YUN S，CHOI J，YOO Y，et al.Action-decision networks for visual tracking with deep reinforcement learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2711-2720.
[85] ZHU Z，HUANG G，ZOU W，et al.Uct：learning unified convolutional networks for real-time visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2017：1973-1982.
[86] HAN B，SIM J，ADAM H.Branchout：regularization for online ensemble tracking with convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：3356-3365.
[87] ZHANG T，XU C，YANG M H.Multi-task correlation particle filter for robust object tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：4335-4343.
[88] WANG N，ZHOU W，TIAN Q，et al.Multi-cue correlation filters for robust visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：4844-4853.
[89] CHOI J，CHANG H J，FISCHER T，et al.Context-aware deep feature compression for high-speed visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：479-488.
[90] DAI K，WANG D，LU H，et al.Visual tracking via adaptive spatially-regularized correlation filters[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：4670-4679.
[91] MA C，HUANG J B，YANG X，et al.Robust visual tracking via hierarchical convolutional features[J].arXiv：1707.03816，2017.
[92] ZHANG M，WANG Q，XING J，et al.Visual tracking via spatially aligned correlation filters network[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：469-485.
[93] WANG M，LIU Y，HUANG Z.Large margin object tracking with circulant feature maps[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：4021-4029.
[94] SUN C，WANG D，LU H，et al.Correlation tracking via joint discrimination and reliability learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：489-497.
[95] SUN C，WANG D，LU H，et al.Learning spatial-aware regressions for visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：8962-8970.
[96] SUN Y，SUN C，WANG D，et al.Roi pooled correlation filters for visual tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：5783-5791.
[97] GUNDOGDU E，ALATAN A A.Good features to correlate for visual tracking[J].IEEE Transactions on Image Processing，2018，27（5）：2526-2540.
[98] XU T，FENG Z H，WU X J，et al.Joint group feature selection and discriminative filter learning for robust visual object tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：7950-7960.
[99] LI F，YAO Y，LI P，et al.Integrating boundary and center correlation filters for visual tracking with aspect ratio variation[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2017：2001-2009.
[100] LI F，TIAN C，ZUO W，et al.Learning spatial-temporal regularized correlation filters for visual tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：4904-4913.
[101] HUANG Z，FU C，LI Y，et al.Learning aberrance repressed correlation filters for real-time uav tracking[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，2019：2891-2900.
[102] LI Y，FU C，DING F，et al.Autotrack：towards high-performance visual tracking for uav with automatic spatio-temporal regularization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：11923-11932.
[103] WU Y，LIM J，YANG M H.Online object tracking：a benchmark[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2013：2411-2418.
[104] WU Y，LIM J，YANG M H.Object tracking benchmark[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37（9）：1834-1848.
[105] KRISTAN M，PFLUGFELDER R，LEONARDIS A，et al.The visual object tracking Vot2013 challenge results[C]//2013 IEEE International Conference on Computer Vision Workshops，2013：98-111.
[106] KRISTAN M，PFLUGFELDER R，LEONARDIS A.The visual object tracking Vot2014 challenge results[C]//European Conference on Computer Vision，2014.
[107] KRISTAN M，MATAS J，LEONARDIS A，et al.The visual object tracking Vot2015 challenge results[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2015：1-23.
[108] KRISTAN M，LEONARDIS A，MATAS J，et al.The eighth visual object tracking Vot2020 challenge results[C]//European Conference on Computer Vision，2020：547-601.
[109] FELSBERG M，BERG A，HAGER G，et al.The thermal infrared visual object tracking Vot-Tir2015 challenge results[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops，2015：76-88.
[110] SMEULDERS A W，CHU D M，CUCCHIARA R，et al.Visual tracking：an experimental survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2013，36（7）：1442-1468.
[111] LIANG P，BLASCH E，LING H.Encoding color information for visual tracking：algorithms and benchmark[J].IEEE Transactions on Image Processing，2015，24（12）：5630-5644.
[112] LI A，LIN M，WU Y，et al.Nus-Pro：a new visual tracking challenge[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，38（2）：335-349.
[113] MUELLER M，SMITH N，GHANEM B.A benchmark and simulator for uav tracking[C]//European Conference on Computer Vision，2016：445-461.
[114] KIANI GALOOGAHI H，FAGG A，HUANG C，et al.Need for speed：a benchmark for higher frame rate object tracking[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：1125-1134.
[115] LI S，YEUNG D Y.Visual object tracking for unmanned aerial vehicles：a benchmark and new motion models[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2017.
[116] VALMADRE J，BERTINETTO L，HENRIQUES J F，et al.Long-term tracking in the wild：a benchmark[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：670-685.
[117] MOUDGIL A，GANDHI V.Long-term visual object tracking benchmark[J].arXiv：1712.01358，2017.
[118] MULLER M，BIBI A，GIANCOLA S，et al.Trackingnet：a large-scale dataset and benchmark for object tracking in the wild[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：300-317.
[119] HUANG L，ZHAO X，HUANG K.Got-10k：a large high-diversity benchmark for generic object tracking in the wild[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2021，43（5）：1562-1577.
[120] FAN H，LIN L，YANG F，et al.Lasot：a high-quality benchmark for large-scale single object tracking[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：5374-5383.