【专知荟萃18】目标跟踪Object Tracking知识资料全集（入门/进阶/论文/综述/视频/专家，附查看）

点击上方“专知”关注获取专业AI知识!

【导读】主题荟萃知识是专知的核心功能之一，为用户提供AI领域系统性的知识学习服务。主题荟萃为用户提供全网关于该主题的精华（Awesome）知识资料收录整理，使得AI从业者便捷学习和解决工作问题！在专知人工智能主题知识树基础上，主题荟萃由专业人工编辑和算法工具辅助协作完成，并保持动态更新！另外欢迎对此创作主题荟萃感兴趣的同学，请加入我们专知AI创作者计划，共创共赢！ 今天专知为大家呈送第十八篇专知主题荟萃-目标跟踪知识资料大全集荟萃 （入门/进阶/综述/视频/代码/专家等），请大家查看！专知访问www.zhuanzhi.ai,  或关注微信公众号后台回复" 专知"进入专知，搜索主题“目标跟踪”查看。此外，我们也提供该文网页桌面手机端（www.zhuanzhi.ai）完整访问，可直接点击访问收录链接地址，以及pdf版下载链接，请文章末尾查看！此为初始版本，请大家指正补充，欢迎在后台留言！欢迎大家分享转发~

• 目标跟踪 (Object Tracking/Visual Tracking) 专知荟萃

• 入门学习

• 进阶文章

• Benchmark

• 综述

• Tutorial

• 代码

• 领域专家

• datasets

入门学习

1.  运动目标跟踪系列（1-17）

• [http://blog.csdn.net/App_12062011/article/category/6269524/1\]

目标跟踪学习笔记（2-4）

• [http://www.cnblogs.com/tornadomeet/archive/2012/03/18/2404817.html]

• [http://www.cnblogs.com/tornadomeet/archive/2012/05/08/2490943.html]

• [http://www.cnblogs.com/tornadomeet/archive/2012/06/23/2559193.html]

目标跟踪算法之深度学习方法

• [https://dragonfive.github.io/2017-04-19/object-tracking-correlation-filter/]

基于深度学习的多目标跟踪算法研究

• [http://www.zte.com.cn/cndata/magazine/zte_communications/2017/4/articles/201708/P020170801275551033095.pdf\]

从传统方法到深度学习，目标跟踪方法的发展概述

• [https://www.jiqizhixin.com/articles/2017-05-14]

目标跟踪算法 Visual Tracking Algorithm Introduction.

• [https://zhuanlan.zhihu.com/visual-tracking]

Online Object Tracking: A Benchmark 论文笔记 和 翻译 - [http://blog.csdn.net/shanglianlm/article/details/47376323], [http://blog.csdn.net/roamer_nuptgczx/article/details/51379191]

计算机视觉中，目前有哪些经典的目标跟踪算法？

• [https://www.zhihu.com/question/26493945]

进阶文章

NIPS2013

• DLT: Naiyan Wang and Dit-Yan Yeung. "Learning A Deep Compact Image Representation for Visual Tracking." NIPS (2013).

• paper [http://winsty.net/papers/dlt.pdf)]

• project [http://winsty.net/dlt.html)]

• code [http://winsty.net/dlt/DLTcode.zip)]

CVPR2014

• CN: Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg and Joost van de Weijer. "Adaptive Color Attributes for Real-Time Visual Tracking." CVPR (2014).

• paper [http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/CN_Tracking_CVPR14.pdf]

• project [http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html]

ECCV2014

• MEEM: Jianming Zhang, Shugao Ma, and Stan Sclaroff. "MEEM: Robust Tracking via Multiple Experts using Entropy Minimization." ECCV (2014).

• paper [http://cs-people.bu.edu/jmzhang/MEEM/MEEM-eccv-preprint.pdf]

• project [http://cs-people.bu.edu/jmzhang/MEEM/MEEM.html]

• TGPR: Jin Gao, Haibin Ling, Weiming Hu, Junliang Xing. "Transfer Learning Based Visual Tracking with Gaussian Process Regression." ECCV (2014).

• paper [http://www.dabi.temple.edu/~hbling/publication/tgpr-eccv14.pdf]

• project [http://www.dabi.temple.edu/~hbling/code/TGPR.htm]

• STC: Kaihua Zhang, Lei Zhang, Ming-Hsuan Yang, David Zhang. "Fast Tracking via Spatio-Temporal Context Learning." ECCV (2014).

• paper [http://arxiv.org/pdf/1311.1939v1.pdf]

• project [http://www4.comp.polyu.edu.hk/~cslzhang/STC/STC.htm]

• SAMF: Yang Li, Jianke Zhu. "A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration." ECCV workshop (2014).

• paper [http://link.springer.com/content/pdf/10.1007%2F978-3-319-16181-5_18.pdf]

• github [https://github.com/ihpdep/samf]

BMVC2014

• DSST: Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan and Michael Felsberg. "Accurate Scale Estimation for Robust Visual Tracking." BMVC (2014).

• paper [http://www.cvl.isy.liu.se/research/objrec/visualtracking/scalvistrack/ScaleTracking_BMVC14.pdf]

• PAMI [http://www.cvl.isy.liu.se/en/research/objrec/visualtracking/scalvistrack/DSST_TPAMI.pdf]

• project [http://www.cvl.isy.liu.se/en/research/objrec/visualtracking/scalvistrack/index.html]

• SAMF: Yang Li, Jianke Zhu. "A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration." ECCV workshop (2014).

• paper [https://github.com/ihpdep/ihpdep.github.io/raw/master/papers/eccvw14_samf.pdf]

• github [https://github.com/ihpdep/samf]

ICML2015

• CNN-SVM: Seunghoon Hong, Tackgeun You, Suha Kwak and Bohyung Han. "Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network ." ICML (2015)

• paper [http://proceedings.mlr.press/v37/hong15.pdf]

• project [http://cvlab.postech.ac.kr/research/CNN_SVM/]

CVPR2015

• MUSTer: Zhibin Hong, Zhe Chen, Chaohui Wang, Xue Mei, Danil Prokhorov, Dacheng Tao. "MUlti-Store Tracker (MUSTer): A Cognitive Psychology Inspired Approach to Object Tracking." CVPR (2015).

• paper [http://openaccess.thecvf.com/content_cvpr_2015/papers/Hong_MUlti-Store_Tracker_MUSTer_2015_CVPR_paper.pdf]

• project [https://sites.google.com/site/multistoretrackermuster/]

• LCT: Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming-Hsuan Yang. "Long-term Correlation Tracking." CVPR (2015).

• paper [http://openaccess.thecvf.com/content_cvpr_2015/papers/Ma_Long-Term_Correlation_Tracking_2015_CVPR_paper.pdf]

• project [https://sites.google.com/site/chaoma99/cvpr15_tracking]

• github [https://github.com/chaoma99/lct-tracker]

• DAT: Horst Possegger, Thomas Mauthner, and Horst Bischof. "In Defense of Color-based Model-free Tracking." CVPR (2015).

• paper [https://lrs.icg.tugraz.at/pubs/possegger_cvpr15.pdf]

• project [https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/dat]

• code [https://lrs.icg.tugraz.at/downloads/dat-v1.0.zip]

• RPT: Yang Li, Jianke Zhu and Steven C.H. Hoi. "Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches." CVPR (2015).

• paper [https://github.com/ihpdep/ihpdep.github.io/raw/master/papers/cvpr15_rpt.pdf]

• github [https://github.com/ihpdep/rpt]

ICCV2015

• FCNT: Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu. "Visual Tracking with Fully Convolutional Networks." ICCV (2015).

• paper [http://ieeexplore.ieee.org/document/7410714/?arnumber=7410714]

• project [http://scott89.github.io/FCNT/]

• github [https://github.com/scott89/FCNT]

• SRDCF: Martin Danelljan, Gustav Häger, Fahad Khan, Michael Felsberg. "Learning Spatially Regularized Correlation Filters for Visual Tracking." ICCV (2015).

• paper [https://www.cvl.isy.liu.se/research/objrec/visualtracking/regvistrack/SRDCF_ICCV15.pdf]

• project [https://www.cvl.isy.liu.se/research/objrec/visualtracking/regvistrack/]

• CF2: Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang. "Hierarchical Convolutional Features for Visual Tracking." ICCV (2015)

• paper [http://faculty.ucmerced.edu/mhyang/papers/iccv15_tracking.pdf]

• project [https://sites.google.com/site/jbhuang0604/publications/cf2]

• github [https://github.com/jbhuang0604/CF2]

• Naiyan Wang, Jianping Shi, Dit-Yan Yeung and Jiaya Jia. "Understanding and Diagnosing Visual Tracking Systems." ICCV (2015).

• paper [http://winsty.net/papers/diagnose.pdf]

• project [http://winsty.net/tracker_diagnose.html]

• code [http://winsty.net/diagnose/diagnose_code.zip]

• DeepSRDCF: Martin Danelljan, Gustav Häger, Fahad Khan, Michael Felsberg. "Convolutional Features for Correlation Filter Based Visual Tracking." ICCV workshop (2015).

• paper [https://www.cvl.isy.liu.se/research/objrec/visualtracking/regvistrack/ConvDCF_ICCV15_VOTworkshop.pdf]

• project [https://www.cvl.isy.liu.se/research/objrec/visualtracking/regvistrack/]

• RAJSSC: Mengdan Zhang, Junliang Xing, Jin Gao, Xinchu Shi, Qiang Wang, Weiming Hu. "Joint Scale-Spatial Correlation Tracking with Adaptive Rotation Estimation." ICCV workshop (2015).

• paper [http://www.cv-foundation.org//openaccess/content_iccv_2015_workshops/w14/papers/Zhang_Joint_Scale-Spatial_Correlation_ICCV_2015_paper.pdf]

• poster [http://www.votchallenge.net/vot2015/download/poster_Mengdan_Zhang.pdf]

NIPS2016

• Learnet: Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi. "Learning feed-forward one-shot learners." NIPS (2016).

• paper [https://arxiv.org/pdf/1606.05233v1.pdf]

CVPR2016

• MDNet: Nam, Hyeonseob, and Bohyung Han. "Learning Multi-Domain Convolutional Neural Networks for Visual Tracking." CVPR (2016).

• paper [https://arxiv.org/abs/1606.09549]

• VOT_presentation [http://votchallenge.net/vot2015/download/presentation_Hyeonseob.pdf]

• project [http://cvlab.postech.ac.kr/research/mdnet/]

• github [https://github.com/HyeonseobNam/MDNet]

• SINT: Ran Tao, Efstratios Gavves, Arnold W.M. Smeulders. "Siamese Instance Search for Tracking." CVPR (2016).

• paper [https://staff.science.uva.nl/r.tao/pub/TaoCVPR2016.pdf]

• project [https://staff.fnwi.uva.nl/r.tao/projects/SINT/SINT_proj.html]

• SCT: Jongwon Choi, Hyung Jin Chang, Jiyeoup Jeong, Yiannis Demiris, and Jin Young Choi. "Visual Tracking Using Attention-Modulated Disintegration and Integration." CVPR (2016).

• paper [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Choi_Visual_Tracking_Using_CVPR_2016_paper.pdf]

• project [https://sites.google.com/site/jwchoivision/home/sct]

• STCT: Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu. "STCT: Sequentially Training Convolutional Networks for Visual Tracking." CVPR (2016).

• paper [http://www.ee.cuhk.edu.hk/~wlouyang/Papers/WangLJ_CVPR16.pdf]

• github [https://github.com/scott89/STCT]

• SRDCFdecon: Martin Danelljan, Gustav Häger, Fahad Khan, Michael Felsberg. "Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking." CVPR (2016).

• paper [https://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/AdaptiveDecon_CVPR16.pdf]

• project [https://www.cvl.isy.liu.se/research/objrec/visualtracking/decontrack/index.html]

• HDT: Yuankai Qi, Shengping Zhang, Lei Qin, Hongxun Yao, Qingming Huang, Jongwoo Lim, Ming-Hsuan Yang. "Hedged Deep Tracking." CVPR (2016).

• paper [http://faculty.ucmerced.edu/mhyang/papers/cvpr16_hedge_tracking.pdf]

• project [https://sites.google.com/site/yuankiqi/hdt/]

• Staple: Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip H.S. Torr. "Staple: Complementary Learners for Real-Time Tracking." CVPR (2016).

• paper [http://www.robots.ox.ac.uk/~luca/staple.html]

• project [http://www.robots.ox.ac.uk/~luca/staple.html]

• github [https://github.com/bertinetto/staple]

• EBT: Gao Zhu, Fatih Porikli, and Hongdong Li. "Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals." CVPR (2016).

• paper [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Beyond_Local_Search_CVPR_2016_paper.pdf]

• exe [http://www.votchallenge.net/vot2016/download/02_EBT.zip]

• DLSSVM: Jifeng Ning, Jimei Yang, Shaojie Jiang, Lei Zhang and Ming-Hsuan Yang. "Object Tracking via Dual Linear Structured SVM and Explicit Feature Map." CVPR (2016).

• paper [http://www4.comp.polyu.edu.hk/~cslzhang/paper/cvpr16/DLSSVM.pdf]

• code [http://www4.comp.polyu.edu.hk/~cslzhang/code/DLSSVM_CVPR.zip]

• project [http://www4.comp.polyu.edu.hk/~cslzhang/DLSSVM/DLSSVM.htm]

ECCV2016

• SiameseFC: Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H.S. Torr. "Fully-Convolutional Siamese Networks for Object Tracking." ECCV workshop (2016).

• paper [http://120.52.73.78/arxiv.org/pdf/1606.09549v2.pdf]

• project [http://www.robots.ox.ac.uk/~luca/siamese-fc.html]

• github [https://github.com/bertinetto/siamese-fc]

• GOTURN: David Held, Sebastian Thrun, Silvio Savarese. "Learning to Track at 100 FPS with Deep Regression Networks." ECCV (2016).

• paper [http://davheld.github.io/GOTURN/GOTURN.pdf]

• project [http://davheld.github.io/GOTURN/GOTURN.html]

• github [https://github.com/davheld/GOTURN]

• C-COT: Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. "Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking." ECCV (2016).

• paper [http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/C-COT_ECCV16.pdf]

• project [http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html]

• github [https://github.com/martin-danelljan/Continuous-ConvOp]

• CF+AT: Adel Bibi, Matthias Mueller, and Bernard Ghanem. "Target Response Adaptation for Correlation Filter Tracking." ECCV (2016).

• paper [http://www.adelbibi.com/papers/ECCV2016/Target_Adap.pdf]

• Yao Sui, Ziming Zhang, Guanghui Wang, Yafei Tang, Li Zhang. "Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning." ECCV (2016).

• paper [http://120.52.73.78/arxiv.org/pdf/1608.08173.pdf]

• Yao Sui, Guanghui Wang, Yafei Tang, Li Zhang. "Tracking Completion." ECCV (2016).

• paper [http://120.52.73.78/arxiv.org/pdf/1608.08171v1.pdf]

CVPR2017

• ECO: Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg. "ECO: Efficient Convolution Operators for Tracking." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Danelljan_ECO_Efficient_Convolution_CVPR_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Danelljan_ECO_Efficient_Convolution_2017_CVPR_supplemental.pdf]

• project [http://www.cvl.isy.liu.se/research/objrec/visualtracking/ecotrack/index.html]

• github [https://github.com/martin-danelljan/ECO]

• CFNet: Jack Valmadre, Luca Bertinetto, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr. "End-to-end representation learning for Correlation Filter based tracking." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Valmadre_End-To-End_Representation_Learning_CVPR_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Valmadre_End-To-End_Representation_Learning_2017_CVPR_supplemental.pdf]

• project [http://www.robots.ox.ac.uk/~luca/cfnet.html]

• github [https://github.com/bertinetto/cfnet]

• CACF: Matthias Mueller, Neil Smith, Bernard Ghanem. "Context-Aware Correlation Filter Tracking." CVPR (2017 oral).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Mueller_Context-Aware_Correlation_Filter_CVPR_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Mueller_Context-Aware_Correlation_Filter_2017_CVPR_supplemental.zip]

• project [https://ivul.kaust.edu.sa/Pages/pub-ca-cf-tracking.aspx]

• code [https://github.com/thias15/Context-Aware-CF-Tracking]

• RaF: Le Zhang, Jagannadan Varadarajan, Ponnuthurai Nagaratnam Suganthan, Narendra Ahuja and Pierre Moulin "Robust Visual Tracking Using Oblique Random Forests." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Robust_Visual_Tracking_CVPR_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Zhang_Robust_Visual_Tracking_2017_CVPR_supplemental.pdf]

• project [https://sites.google.com/site/zhangleuestc/incremental-oblique-random-forest]

• code [https://github.com/ZhangLeUestc/Incremental-Oblique-Random-Forest]

• MCPF: Tianzhu Zhang, Changsheng Xu, Ming-Hsuan Yang. "Multi-Task Correlation Particle Filter for Robust Object Tracking." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Multi-Task_Correlation_Particle_CVPR_2017_paper.pdf]

• project [http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/mcpf.html]

• code [http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/mcpf.html]

• ACFN: Jongwon Choi, Hyung Jin Chang, Sangdoo Yun, Tobias Fischer, Yiannis Demiris, and Jin Young Choi. "Attentional Correlation Filter Network for Adaptive Visual Tracking." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Choi_Attentional_Correlation_Filter_CVPR_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Choi_Attentional_Correlation_Filter_2017_CVPR_supplemental.pdf]

• project [https://sites.google.com/site/jwchoivision/home/acfn-1]

• test code [https://drive.google.com/file/d/0B0ZkG8zaRQoLQUswbW9qSWFaU0U/view?usp=drive_web]

• training code [https://drive.google.com/file/d/0B0ZkG8zaRQoLZVVranBnbHlydnM/view?usp=drive_web]

• LMCF: Mengmeng Wang, Yong Liu, Zeyi Huang. "Large Margin Object Tracking with Circulant Feature Maps." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Large_Margin_Object_CVPR_2017_paper.pdf]

• zhihu [https://zhuanlan.zhihu.com/p/25761718]

• ADNet: Sangdoo Yun, Jongwon Choi, Youngjoon Yoo, Kimin Yun, Jin Young Choi. "Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning." CVPR (2017 Spotlight).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Yun_Action-Decision_Networks_for_CVPR_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Yun_Action-Decision_Networks_for_2017_CVPR_supplemental.pdf]

• project [https://sites.google.com/view/cvpr2017-adnet]

• CSR-DCF: Alan Lukežič, Tomáš Vojíř, Luka Čehovin, Jiří Matas, Matej Kristan. "Discriminative Correlation Filter with Channel and Spatial Reliability." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Lukezic_Discriminative_Correlation_Filter_CVPR_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Lukezic_Discriminative_Correlation_Filter_2017_CVPR_supplemental.pdf]

• code [https://github.com/alanlukezic/csr-dcf]

• BranchOut: Bohyung Han, Jack Sim, Hartwig Adam. "BranchOut: Regularization for Online Ensemble Tracking with Convolutional Neural Networks." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Han_BranchOut_Regularization_for_CVPR_2017_paper.pdf]

• AMCT: Donghun Yeo, Jeany Son, Bohyung Han, Joonhee Han. "Superpixel-based Tracking-by-Segmentation using Markov Chains." CVPR (2017).

• paper [http://openaccess.thecvf.com/content_cvpr_2017/papers/Yeo_Superpixel-Based_Tracking-By-Segmentation_Using_CVPR_2017_paper.pdf]

• SANet: Heng Fan, Haibin Ling. "SANet: Structure-Aware Network for Visual Tracking." CVPRW (2017).

• paper [https://arxiv.org/pdf/1611.06878.pdf]

• project [http://www.dabi.temple.edu/~hbling/code/SANet/SANet.html]

• code [http://www.dabi.temple.edu/~hbling/code/SANet/sanet_code.zip]

ICCV2017

• CREST: Yibing Song, Chao Ma, Lijun Gong, Jiawei Zhang, Rynson Lau, Ming-Hsuan Yang. "CREST: Convolutional Residual Learning for Visual Tracking." ICCV (2017 Spotlight).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Song_CREST_Convolutional_Residual_ICCV_2017_paper.pdf]

• project [http://www.cs.cityu.edu.hk/~yibisong/iccv17/index.html]

• github [https://github.com/ybsong00/CREST-Release]

• EAST: Chen Huang, Simon Lucey, Deva Ramanan. "Learning Policies for Adaptive Tracking with Deep Feature Cascades." ICCV (2017 Spotlight).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_Learning_Policies_for_ICCV_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_ICCV_2017/supplemental/Huang_Learning_Policies_for_ICCV_2017_supplemental.zip]

• PTAV: Heng Fan and Haibin Ling. "Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking." ICCV (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Fan_Parallel_Tracking_and_ICCV_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_ICCV_2017/supplemental/Fan_Parallel_Tracking_and_ICCV_2017_supplemental.pdf]

• project [http://www.dabi.temple.edu/~hbling/code/PTAV/ptav.htm]

• code [http://www.dabi.temple.edu/~hbling/code/PTAV/serial_ptav_v1.zip]

• BACF: Hamed Kiani Galoogahi, Ashton Fagg, Simon Lucey. "Learning Background-Aware Correlation Filters for Visual Tracking." ICCV (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Galoogahi_Learning_Background-Aware_Correlation_ICCV_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_ICCV_2017/supplemental/Galoogahi_Learning_Background-Aware_Correlation_ICCV_2017_supplemental.pdf]

• TSN: Zhu Teng, Junliang Xing, Qiang Wang, Congyan Lang, Songhe Feng and Yi Jin. "Robust Object Tracking based on Temporal and Spatial Deep Networks." ICCV (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Teng_Robust_Object_Tracking_ICCV_2017_paper.pdf]

• p-tracker: James Supančič, III; Deva Ramanan. "Tracking as Online Decision-Making: Learning a Policy From Streaming Videos With Reinforcement Learning." ICCV (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Supancic_Tracking_as_Online_ICCV_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_ICCV_2017/supplemental/Supancic_Tracking_as_Online_ICCV_2017_supplemental.pdf]

• DSiam: Qing Guo; Wei Feng; Ce Zhou; Rui Huang; Liang Wan; Song Wang. "Learning Dynamic Siamese Network for Visual Object Tracking." ICCV (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Guo_Learning_Dynamic_Siamese_ICCV_2017_paper.pdf]

• SP-KCF: Xin Sun; Ngai-Man Cheung; Hongxun Yao; Yiluan Guo. "Non-Rigid Object Tracking via Deformable Patches Using Shape-Preserved KCF and Level Sets." ICCV (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Sun_Non-Rigid_Object_Tracking_ICCV_2017_paper.pdf]

• UCT: Zheng Zhu, Guan Huang, Wei Zou, Dalong Du, Chang Huang. "UCT: Learning Unified Convolutional Networks for Real-Time Visual Tracking." ICCV workshop (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/Zhu_UCT_Learning_Unified_ICCV_2017_paper.pdf]

• Tobias Bottger, Patrick Follmann. "The Benefits of Evaluating Tracker Performance Using Pixel-Wise Segmentations." ICCV workshop (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/Bottger_The_Benefits_of_ICCV_2017_paper.pdf]

• CFWCR: Zhiqun He, Yingruo Fan, Junfei Zhuang, Yuan Dong, HongLiang Bai. "Correlation Filters With Weighted Convolution Responses." ICCV workshop (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/He_Correlation_Filters_With_ICCV_2017_paper.pdf]

• IBCCF: Feng Li, Yingjie Yao, Peihua Li, David Zhang, Wangmeng Zuo, Ming-Hsuan Yang. "Integrating Boundary and Center Correlation Filters for Visual Tracking With Aspect Ratio Variation." ICCV workshop (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/Li_Integrating_Boundary_and_ICCV_2017_paper.pdf]

• RFL: Tianyu Yang, Antoni B. Chan. "Recurrent Filter Learning for Visual Tracking." ICCV workshop (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/Yang_Recurrent_Filter_Learning_ICCV_2017_paper.pdf]

PAMI & IJCV & TIP

• KCF: João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista. "High-Speed Tracking with Kernelized Correlation Filters." TPAMI (2015).

• paper [http://www.robots.ox.ac.uk/~joao/publications/henriques_tpami2015.pdf]

• project [http://www.robots.ox.ac.uk/~joao/circulant/]

• CLRST: Tianzhu Zhang, Si Liu, Narendra Ahuja, Ming-Hsuan Yang, Bernard Ghanem.
  "Robust Visual Tracking Via Consistent Low-Rank Sparse Learning." IJCV (2015).

• paper [http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/tianzhu%20zhang_files/Journal%20Articles/IJCV15_zhang_Low-Rank%20Sparse%20Learning.pdf]

• project [http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/Project_Tianzhu/zhang_IJCV14/Robust%20Visual%20Tracking%20Via%20Consistent%20Low-Rank%20Sparse.html]

• code [http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/Project_Tianzhu/zhang_IJCV14/material/LRT_Code.zip]

• DNT: Zhizhen Chi, Hongyang Li, Huchuan Lu, Ming-Hsuan Yang. "Dual Deep Network for Visual Tracking." TIP (2017).

• paper [https://arxiv.org/pdf/1612.06053v1.pdf]

• DRT: Junyu Gao, Tianzhu Zhang, Xiaoshan Yang, Changsheng Xu. "Deep Relative Tracking." TIP (2017).

• paper [http://ieeexplore.ieee.org/abstract/document/7828108/]

• BIT: Bolun Cai, Xiangmin Xu, Xiaofen Xing, Kui Jia, Jie Miao, Dacheng Tao. "BIT: Biologically Inspired Tracker." TIP (2016).

• paper [http://caibolun.github.io/papers/BIT_TIP.pdf]

• project [http://caibolun.github.io/BIT/index.html]

• github [https://github.com/caibolun/BIT]

• CNT: Kaihua Zhang, Qingshan Liu, Yi Wu, Minghsuan Yang. "Robust Visual Tracking via Convolutional Networks Without Training." TIP (2016).

• paper [http://kaihuazhang.net/CNT.pdf]

• code [http://kaihuazhang.net/CNT_matlab.rar]

ArXiv

• PAWSS: Xiaofei Du, Alessio Dore, Danail Stoyanov. "Patch-based adaptive weighting with segmentation and scale (PAWSS) for visual tracking." arXiv (2017).

• paper [https://arxiv.org/pdf/1708.01179v1.pdf]

• SFT: Zhen Cui, You yi Cai, Wen ming Zheng, Jian Yang. "Spectral Filter Tracking." arXiv (2017).

• paper [https://arxiv.org/pdf/1707.05553v1.pdf]

• HART: Adam R. Kosiorek, Alex Bewley, Ingmar Posner. "Hierarchical Attentive Recurrent Tracking." arXiv (2017).

• paper [https://arxiv.org/pdf/1706.09262.pdf]

• github [https://github.com/akosiorek/hart]

• Re3: Daniel Gordon, Ali Farhadi, Dieter Fox. "Re3 : Real-Time Recurrent Regression Networks for Object Tracking." arXiv (2017).

• paper [https://arxiv.org/pdf/1705.06368.pdf]

• DCFNet: Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu. "DCFNet: Discriminant Correlation Filters Network for Visual Tracking." arXiv (2017).

• paper [https://arxiv.org/pdf/1704.04057.pdf]

• code [https://github.com/foolwood/DCFNet#dcfnet-discriminant-correlation-filters-network-for-visual-tracking]

• TCNN: Hyeonseob Nam, Mooyeol Baek, Bohyung Han. "Modeling and Propagating CNNs in a Tree Structure for Visual Tracking." arXiv (2016).

• paper [http://arxiv.org/pdf/1608.07242v1.pdf]

• code [http://www.votchallenge.net/vot2016/download/44_TCNN.zip]

• RDT: Janghoon Choi, Junseok Kwon, Kyoung Mu Lee. "Visual Tracking by Reinforced Decision Making." arXiv (2017).

• paper [https://arxiv.org/pdf/1702.06291.pdf]

• MSDAT: Xinyu Wang, Hanxi Li, Yi Li, Fumin Shen, Fatih Porikli . "Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation." arXiv (2017).

• paper [https://arxiv.org/pdf/1701.00561.pdf]

• RLT: Da Zhang, Hamid Maei, Xin Wang, Yuan-Fang Wang. "Deep Reinforcement Learning for Visual Object Tracking in Videos." arXiv (2017).

• paper](https://arxiv.org/pdf/1701.08936v1.pdf]

• SCF: Wangmeng Zuo, Xiaohe Wu, Liang Lin, Lei Zhang, Ming-Hsuan Yang. "Learning Support Correlation Filters for Visual Tracking." arXiv (2016).

• paper [https://arxiv.org/pdf/1601.06032.pdf]

• project [http://faculty.ucmerced.edu/mhyang/project/scf/]

• CRT: Kai Chen, Wenbing Tao. "Convolutional Regression for Visual Tracking." arXiv (2016).

• paper](https://arxiv.org/pdf/1611.04215.pdf]

• BMR: Kaihua Zhang, Qingshan Liu, and Ming-Hsuan Yang. "Visual Tracking via Boolean Map Representations." arXiv (2016).

• paper](https://arxiv.org/pdf/1610.09652v1.pdf]

• YCNN: Kai Chen, Wenbing Tao. "Once for All: a Two-flow Convolutional Neural Network for Visual Tracking." arXiv (2016).

• paper](https://arxiv.org/pdf/1604.07507v1.pdf]

• ROLO: Guanghan Ning, Zhi Zhang, Chen Huang, Zhihai He, Xiaobo Ren, Haohong Wang. "Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking." arXiv (2016).

• paper [http://arxiv.org/pdf/1607.05781v1.pdf]

• project [http://guanghan.info/projects/ROLO/]

• github [https://github.com/Guanghan/ROLO/]

• RATM: Samira Ebrahimi Kahou, Vincent Michalski, Roland Memisevic. "RATM: Recurrent Attentive Tracking Model." arXiv (2015).

• paper [https://arxiv.org/pdf/1510.08660v4.pdf]

• github [https://github.com/saebrahimi/RATM]

• SO-DLT: Naiyan Wang, Siyi Li, Abhinav Gupta, Dit-Yan Yeung. "Transferring Rich Feature Hierarchies for Robust Visual Tracking." arXiv (2015).

• paper [https://arxiv.org/pdf/1501.04587v2.pdf]

• code [http://www.votchallenge.net/vot2016/download/08_SO-DLT.zip]

• DMSRDCF: Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg. "Deep Motion Features for Visual Tracking." ICPR Best Paper (2016).

• paper](https://arxiv.org/pdf/1612.06615v1.pdf]

Benchmark

• Dataset-AMP: Luka Čehovin Zajc; Alan Lukežič; Aleš Leonardis; Matej Kristan. "Beyond Standard Benchmarks: Parameterizing Performance Evaluation in Visual Object Tracking." ICCV (2017).

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Zajc_Beyond_Standard_Benchmarks_ICCV_2017_paper.pdf]

• Dataset-Nfs: Hamed Kiani Galoogahi, Ashton Fagg, Chen Huang, Deva Ramanan and Simon Lucey. "Need for Speed: A Benchmark for Higher Frame Rate Object Tracking." ICCV (2017)

• paper [http://openaccess.thecvf.com/content_ICCV_2017/papers/Galoogahi_Need_for_Speed_ICCV_2017_paper.pdf]

• supp [http://openaccess.thecvf.com/content_ICCV_2017/supplemental/Galoogahi_Need_for_Speed_ICCV_2017_supplemental.pdf]

• project [http://ci2cv.net/nfs/index.html]

• Dataset-DTB70: Siyi Li, Dit-Yan Yeung. "Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models." AAAI (2017)

• paper [http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14338/14292]

• project [https://github.com/flyers/drone-tracking]

• dataset [https://www.dropbox.com/s/s1fj99s2six4lrs/DTB70.tar.gz?dl=0]

• Dataset-UAV123: Matthias Mueller, Neil Smith and Bernard Ghanem. "A Benchmark and Simulator for UAV Tracking." ECCV (2016)

• paper [https://ivul.kaust.edu.sa/Documents/Publications/2016/A%20Benchmark%20and%20Simulator%20for%20UAV%20Tracking.pdf]

• project [https://ivul.kaust.edu.sa/Pages/pub-benchmark-simulator-uav.aspx]

• dataset [https://ivul.kaust.edu.sa/Pages/Dataset-UAV123.aspx]

• Dataset-TColor-128: Pengpeng Liang, Erik Blasch, Haibin Ling. "Encoding color information for visual tracking: Algorithms and benchmark." TIP (2015)

• paper [http://www.dabi.temple.edu/~hbling/publication/TColor-128.pdf]

• project [http://www.dabi.temple.edu/~hbling/data/TColor-128/TColor-128.html]

• dataset [http://www.dabi.temple.edu/~hbling/data/TColor-128/Temple-color-128.zip]

• Dataset-NUS-PRO: Annan Li, Min Lin, Yi Wu, Ming-Hsuan Yang, and Shuicheng Yan. "NUS-PRO: A New Visual Tracking Challenge." PAMI (2015)

• paper [http://faculty.ucmerced.edu/mhyang/papers/pami15_nus_pro.pdf]

• project [https://sites.google.com/site/li00annan/nus-pro]

• Data_360 [https://d9fca6.lc.yunpan.cn/lk/cqKIc6DU3t2eJcode:bf28))(]

• Data_baidu [https://pan.baidu.com/s/1pJHvbSn#list/path=%2F]

• View_360 [https://6aa275.lc.yunpan.cn/lk/cqK479PfzDrPXcode:515a)(]

• View_baidu [https://pan.baidu.com/s/1hqKXcuK]

• Dataset-PTB: Shuran Song and Jianxiong Xiao. "Tracking Revisited using RGBD Camera: Unified Benchmark and Baselines." ICCV (2013)

• paper [http://vision.princeton.edu/projects/2013/tracking/paper.pdf]

• project [http://tracking.cs.princeton.edu/]

• 5 validation [http://tracking.cs.princeton.edu/ValidationSet.zip]

• 95 evaluation [http://tracking.cs.princeton.edu/EvaluationSet.tgz]

• Dataset-ALOV300+: Arnold W. M. Smeulders, Dung M. Chu, Rita Cucchiara, Simone Calderara, Afshin Dehghan, Mubarak Shah. "Visual Tracking: An Experimental Survey." PAMI (2014)

• paper [http://crcv.ucf.edu/papers/Tracking_Survey.pdf]

• project [http://imagelab.ing.unimore.it/dsm/]

• Mirror Link:ALOV300++ Dataset [http://crcv.ucf.edu/people/phd_students/afshin/ALOV300/Frames.zip]

• Mirror Link:ALOV300++ Groundtruth [http://crcv.ucf.edu/people/phd_students/afshin/ALOV300/GT.zip]

• OTB2013: Wu, Yi, Jongwoo Lim, and Minghsuan Yang. "Online Object Tracking: A Benchmark." CVPR (2013).

• paper [http://faculty.ucmerced.edu/mhyang/papers/cvpr13_benchmark.pdf]

• OTB2015: Wu, Yi, Jongwoo Lim, and Minghsuan Yang. "Object Tracking Benchmark." TPAMI (2015).

• paper [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7001050&tag=1]

• project [http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html]

• Dataset-VOT: project [http://www.votchallenge.net/]

• VOT13_paper_ICCV
  [http://www.votchallenge.net/vot2013/Download/vot_2013_paper.pdf]
  The Visual Object Tracking VOT2013 challenge results

• VOT14_paper_ECCV
  [http://www.votchallenge.net/vot2014/download/vot_2014_paper.pdf]
  The Visual Object Tracking VOT2014 challenge results

• VOT15_paper_ICCV
  [http://www.votchallenge.net/vot2015/download/vot_2015_paper.pdf]
  The Visual Object Tracking VOT2015 challenge results

• VOT16_paper_ECCV
  [http://www.votchallenge.net/vot2016/download/vot_2016_paper.pdf]
  The Visual Object Tracking VOT2016 challenge results

• VOT17_paper_ECCV
  [http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/Kristan_The_Visual_Object_ICCV_2017_paper.pdf]
  The Visual Object Tracking VOT2017 challenge results

综述

1. Visual Tracking: An Experimental Survey. PAMI2014.
   - [http://ieeexplore.ieee.org/document/6671560/], [https://dl.acm.org/citation.cfm?id=2693387]
   - 代码：[http://alov300pp.joomlafree.it/trackers-resource.html]

2. Online Object Tracking: A Benchmark CVPR2013: Wu Y, Lim J, Yang M H.
   - 网址和代码：[http://cvlab.hanyang.ac.kr/tracker_benchmark/benchmark_v10.html]

3. A survey of datasets for visual tracking
   - [https://link.springer.com/article/10.1007/s00138-015-0713-y]

4. Siamese Learning Visual Tracking: A Survey

• [https://arxiv.org/abs/1707.00569]
  

A survey on multiple object tracking algorithm

• [http://ieeexplore.ieee.org/document/7832121/]

Tutorial

1. Object Tracking

• [https://www.ssontech.com/tutes/tuteobj.html]

Stanford cs231b Lecture 5: Visual Tracking by Alexandre Alahi Stanford Vision Lab

• [http://vision.stanford.edu/teaching/cs231b_spring1415/slides/lectureTracking.pdf\]

代码

1. Hierarchical Convolutional Features for Visual Tracking

• [https://github.com/jbhuang0604/CF2]

Robust Visual Tracking via Convolutional Networks

• [http://kaihuazhang.net/CNT_matlab.rar\]

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

• [https://github.com/HyeonseobNam/MDNet]

Understanding and Diagnosing Visual Tracking Systems

• [http://120.52.72.43/winsty.net/c3pr90ntcsf0/diagnose/diagnose_code.zip\]

Visual Tracking with Fully Convolutional Networks

• [https://github.com/scott89/FCNT]

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

• [https://github.com/pondruska/DeepTracking]

Learning to Track at 100 FPS with Deep Regression Networks

• [https://github.com/davheld/GOTURN]

Fully-Convolutional Siamese Networks for Object Tracking

• [https://github.com/torrvision/siamfc-tf]

Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

• [https://github.com/Guanghan/ROLO]

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

• [https://github.com/braincorp/PVM]

ECO: Efficient Convolution Operators for Tracking

• [https://github.com/martin-danelljan/ECO]

End-to-end representation learning for Correlation Filter based tracking

• [https://github.com/bertinetto/cfnet]

Context-Aware Correlation Filter Tracking

• [https://github.com/thias15/Context-Aware-CF-Tracking]

CREST: Convolutional Residual Learning for Visual Tracking

• [https://github.com/ybsong00/CREST-Release]

中科院自动化所胡卫明老师组的博士生王强整理的一些benchmark结果以及论文汇总（好多是参考他的，再次感谢）
- [https://github.com/foolwood/benchmark_results]

Benchmark Results of Correlation Filters, 相关滤波这几年在tracking领域应用非常广，效果也很惊人，这是总结的近几年相关的文章，上面进阶文章大多数都有了，但是这个Github链接 把CF 变形的方法都罗列分类的很齐全，建议收藏。
- [https://github.com/HakaseH/CF_benchmark_results]

领域专家

1. Ming-Hsuan Yang[http://faculty.ucmerced.edu/mhyang/]

• Robust Visual Tracking via Consistent Low-Rank Sparse Learning

• FCT,IJCV2014:Fast Compressive Tracking

• RST,PAMI2014:Robust Superpixel Tracking; SPT,ICCV2011, Superpixeltracking

• SVD,TIP2014:Learning Structured Visual Dictionary for Object Tracking

• ECCV2014: Spatio temporalBackground Subtraction Using Minimum Spanning Tree and Optical Flow

• PAMI2011:Robust Object Tracking with Online Multiple Instance Learning

• MIT,CVPR2009: Visual tracking with online multiple instance learning

• IJCV2008: Incremental Learning for Robust Visual Tracking

• Ming-HsuanYang视觉跟踪当之无愧第一人，后面的人基本上都和其有合作关系，他引已上万

Haibin Ling

• [http://www.dabi.temple.edu/~hbling/]

Huchuan Lu

• [http://ice.dlut.edu.cn/lu/]

Hongdong Li

• [http://users.cecs.anu.edu.au/~hongdong/]

Lei Zhang

• [http://www4.comp.polyu.edu.hk/~cslzhang/]

Xiaogang Wang

• [http://www.ee.cuhk.edu.hk/~xgwang/]

Matej Kristan

• [http://www.vicos.si/People/Matejk]

João F. Henriques

• [http://www.robots.ox.ac.uk/~joao/]

Martin Danelljan

• [http://users.isy.liu.se/cvl/marda26/]

Kaihua Zhang

• [http://kaihuazhang.net/]

Hamed Kiani

• [http://www.hamedkiani.com/]

Luca Bertinetto

• [http://www.robots.ox.ac.uk/~luca/index.html]

Tianzhu Zhang

• [http://nlpr-web.ia.ac.cn/mmc/homepage/tzzhang/index.html]

datasets

1. OTB

• [http://cvlab.hanyang.ac.kr/tracker_benchmark/\]

VOT

• [http://www.votchallenge.net/]

初步版本，水平有限，有错误或者不完善的地方，欢迎大家提建议和补充（到专知网站www.zhuanzhi.ai 主题下评论），会一直保持更新，敬请关注http://www.zhuanzhi.ai 和关注专知公众号，获取最新AI相关知识。

欢迎转发分享专业AI知识！

特别提示-专知目标跟踪主题：

请PC登录www.zhuanzhi.ai或者点击阅读原文，注册登录，顶端搜索“目标跟踪” 主题，查看获得专知荟萃全集知识等资料，直接PC端访问体验更佳！如下图所示~

此外，请关注专知公众号（扫一扫最下面专知二维码，或者点击上方蓝色专知），

• 后台回复“目标跟踪”或者“OT” 就可以在手机端获取专知目标跟踪资料查看链接地址，直接打开荟萃资料的链接地址~~

请扫描专知小助手，加入专知人工智能群交流~

往期专知荟萃知识资料全集获取（关注本公众号-专知，获取下载链接），请查看：

-END-

欢迎使用专知

专知，一个新的认知方式！专注在人工智能领域为AI从业者提供专业可信的知识分发服务, 包括主题定制、主题链路、搜索发现等服务，帮你又好又快找到所需知识。

使用方法>>访问www.zhuanzhi.ai, 或点击文章下方“阅读原文”即可访问专知

中国科学院自动化研究所专知团队

@2017 专知

专 · 知

关注我们的公众号，获取最新关于专知以及人工智能的资讯、技术、算法、深度干货等内容。扫一扫下方关注我们的微信公众号。

点击“阅读原文”，使用专知！