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visual tracker benchmark results

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OTB Results

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recent_develop OTB2013_results TB-100_results TB-50_results

Benchmark Results

The trackers are ordered by the average overlap scores.

  • AUC and Precision are the standard metrics.
  • Deep Learning: deep learning features, deep learning method and RL.
  • RealTime: Speeds from the original paper, not test on the same platform. (just focus magnitude)
Tracker AUC-CVPR2013 Precision-CVPR2013 AUC-OTB100 Precision-OTB100 AUC-OTB50 Precision-OTB50 Deep Learning RealTime
ECO 0.709 0.93 0.694 0.910 0.643 0.874 Y N(6)
MDNet 0.708 0.948 0.678 0.909 0.645 0.890 Y N(1)
SANet 0.686 0.95 0.692 0.928 - - Y N(1)
BranchOut 0.678 0.917 Y N(1)
TCNN 0.682 0.937 0.654 0.884 - - Y N(1)
TSN 0.644 0.868 0.58 0.809 Y N(1)
CRT - - 0.642 0.875 0.594 0.835 Y N(1.3)
BACF 0.678 0.63 N Y(35)
MCPF 0.677 0.916 0.628 0.873 Y N(0.5)
CREST 0.673 0.908 0.623 0.837 - - Y N(1)
C-COT 0.672 0.899 0.682 - - - Y N(0.3)
DNT 0.664 0.907 0.627 0.851 - - Y N(5)
PTAV 0.663 0.894 0.635 0.849 Y Y(25)
ADNet 0.659 0.903 0.646 0.88 Y N(3)
DSiamM 0.656 0.891 Y Y(25)
SINT+ 0.655 0.882 - - - - Y N(4)
DRT 0.655 0.892 - - - - Y N(0.8)
RDT 0.654 - 0.603 - - - Y Y(43)
SRDCFdecon 0.653 0.87 0.627 0.825 0.56 0.764 N N(1)
DeepLMCF 0.643 0.892 Y N(8)
MUSTer 0.641 0.865 0.575 0.774 - - N N(4)
DeepSRDCF 0.641 0.849 0.635 0.851 0.56 0.772 Y N(<1)
EAST 0.638 Y Y(23/159)
SINT 0.635 0.851 - - - - Y N(4)
LCT 0.628 0.848 0.562 0.762 0.492 0.691 N Y(27)
SRDCF 0.626 0.838 0.598 0.789 0.539 0.732 N N(5)
LMCF 0.624 0.839 0.568 N Y(85)
SCF 0.623 0.874 - - - - N Y(35)
Staple_CA 0.621 0.833 0.598 0.81 N Y(35)
RaF 0.615 0.919 Y N(2)
SiamFC 0.612 0.815 - - - - Y Y(58)
RFL 0.581 Y Y(15)
CFNet_conv2 0.611 0.807 0.568 0.748 0.53 0.702 Y Y(75)
SiamFC_{3s} 0.608 0.809 - - - - Y Y(86)
ACFN 0.607 0.86 0.575 0.802 Y Y(15)
CF2 0.605 0.891 0.562 0.837 0.513 0.803 Y N(11)
HDT 0.603 0.889 0.654 0.848 0.515 0.804 Y N(10)
Staple 0.6 0.793 0.578 0.784 - - N Y(80)
CSR-DCF 0.599 0.8 0.598 0.733 N Y(13)
FCNT 0.599 0.856 - - - - Y N(1)
CNN-SVM 0.597 0.852 0.554 0.814 0.512 0.769 Y N
SCT 0.595 0.845 - - - - Y Y(40)
SO-DLT 0.595 0.81 - - - - Y N
BIT 0.593 0.817 - - - - N Y(45)
DLSSVM 0.589 0.829 0.541 0.767 - - Y N(10)
SAMF 0.579 0.785 0.535 0.743 - - N N(7)
RPT 0.577 0.805 - - - - N N(4)
MEEM 0.566 0.83 0.53 0.781 0.473 0.712 N N(10)
DSST 0.554 0.737 0.52 0.693 0.463 0.625 N Y(24)
CNT 0.545 0.723 - - - - Y N(1.5)
TGPR 0.529 0.766 0.458 0.643 - - N N(1)
KCF 0.514 0.74 0.477 0.693 0.403 0.611 N Y(172)
GOTURN 0.444 0.62 0.427 0.572 - - Y Y(165)

Visual Trackers

CVPR2018

  • VITAL: Yibing Song, Chao Ma, Xiaohe Wu, Lijun Gong, Linchao Bao, Wangmeng Zuo, Chunhua Shen, Rynson Lau, and Ming-Hsuan Yang. "VITAL: VIsual Tracking via Adversarial Learning." CVPR (2018 Spotlight). [project] [paper] [github]

  • LSART: Chong Sun, Dong Wang, Huchuan Lu, Ming-Hsuan Yang. "Learning Spatial-Aware Regressions for Visual Tracking." CVPR (2018 Spotlight). [paper]

  • SiamRPN: Bo Li, Wei Wu, Zheng Zhu, Junjie Yan. " High Performance Visual Tracking with Siamese Region Proposal Network." CVPR (2018 Spotlight).

  • TRACA: Jongwon Choi, Hyung Jin Chang, Tobias Fischer, Sangdoo Yun, Kyuewang Lee, Jiyeoup Jeong, Yiannis Demiris, Jin Young Choi. "Context-aware Deep Feature Compression for High-speed Visual Tracking." CVPR (2018). [project]

  • RASNet: Qiang Wang, Zhu Teng, Junliang Xing, Jin Gao, Weiming Hu, Stephen Maybank. "Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking." CVPR 2018. [paper]

  • SA-Siam: Anfeng He, Chong Luo, Xinmei Tian, Wenjun Zeng. "A Twofold Siamese Network for Real-Time Object Tracking." CVPR (2018). [paper]

  • STRCF: Feng Li, Cheng Tian, Wangmeng Zuo, Lei Zhang, Ming-Hsuan Yang. "Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking." CVPR (2018). [paper] [github]

  • FlowTrack: Zheng Zhu, Wei Wu, Wei Zou, Junjie Yan. "End-to-end Flow Correlation Tracking with Spatial-temporal Attention." CVPR (2018). [paper]

  • : Kourosh Meshgi, Shigeyuki Oba, Shin Ishii. "Efficient Diverse Ensemble for Discriminative Co-Tracking." CVPR (2018). [paper]

  • SINT++: Xiao Wang, Chenglong Li, Bin Luo, Jin Tang. "SINT++: Robust Visual Tracking via Adversarial Hard Positive Generation." CVPR (2018).

  • : Chong Sun, Dong Wang, Huchuan Lu, Ming-Hsuan Yang. "Correlation Tracking via Joint Discrimination and Reliability Learning." CVPR (2018).

  • : Ning Wang, Wengang Zhou, Qi Tian, Richang Hong, Meng Wang, Houqiang Li. "Multi-Cue Correlation Filters for Robust Visual Tracking." CVPR (2018).

  • : Ming Tang, Bin Yu, Fan Zhang, Jinqiao Wang. "High-speed Tracking with Multi-kernel Correlation Filters." CVPR (2018).

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] [project] [github]

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

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

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

  • 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]

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

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

  • 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]

  • 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]

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

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

  • 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] [github]

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

CVPR2017

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

  • 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] [supp] [project] [github]

  • CACF: Matthias Mueller, Neil Smith, Bernard Ghanem. "Context-Aware Correlation Filter Tracking." CVPR (2017 oral). [paper] [supp] [project] [code]

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

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

  • 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] [supp] [project] [test code] [training code]

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

  • 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] [supp] [project]

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

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

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

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

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] [project] [github]

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

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

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

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

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

CVPR2016

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

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

  • 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] [project]

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

  • 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] [project]

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

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

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

  • 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] [code] [project]

NIPS2016

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

ICCV2015

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

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

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

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

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

  • 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] [poster]

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] [project]

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

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

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

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] [project]

BMVC2014

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

ECCV2014

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

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

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

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

NIPS2013

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

PAMI & IJCV & TIP

  • AOGTracker: Tianfu Wu , Yang Lu and Song-Chun Zhu. "Online Object Tracking, Learning and Parsing with And-Or Graphs." TPAMI (2017). [paper] [project] [github]

  • MCPF: Tianzhu Zhang, Changsheng Xu, Ming-Hsuan Yang. " Learning Multi-task Correlation Particle Filters for Visual Tracking." TPAMI (2017). [[paper]] [project] [code]

  • RSST: Tianzhu Zhang, Changsheng Xu, Ming-Hsuan Yang. " Robust Structural Sparse Tracking." TPAMI (2017). [[paper]] [project] [code]

  • fDSST: Martin Danelljan, Gustav Häger, Fahad Khan, Michael Felsberg. "Discriminative Scale Space Tracking." TPAMI (2017). [paper] [project] [code]

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

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

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

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

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

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

ArXiv

  • : Goutam Bhat, Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg. "Unveiling the Power of Deep Tracking." arXiv (2018). [paper]

  • Meta-Tracker: Eunbyung Park, Alexander C. Berg. "Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers." arXiv (2018). [paper] [github]

  • MLT: Janghoon Choi, Junseok Kwon, Kyoung Mu Lee. "Deep Meta Learning for Real-Time Visual Tracking based on Target-Specific Feature Space." arXiv (2017). [paper]

  • STECF: Yang Li, Jianke Zhu, Wenjie Song, Zhefeng Wang, Hantang Liu, Steven C. H. Hoi. "Robust Estimation of Similarity Transformation for Visual Object Tracking with Correlation Filters." arXiv (2017). [paper]

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

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

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

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

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

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

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

  • 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]

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

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

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

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

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

  • 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] [project] [github]

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

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

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

Benchmark

  • TrackingNet: Matthias Müller, Adel Bibi, Silvio Giancola, Salman Al-Subaihi, Bernard Ghanem. "TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild". [paper]

  • OxUvA long-term dataset+benchmark: Jack Valmadre*, Luca Bertinetto*, João F. Henriques, Ran Tao, Arnold Smeulders, Andrea Vedaldi, Philip Torr, Efstratios Gavves*.
    "Long-term Tracking in the Wild: A Benchmark". [paper] [project]

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

  • 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] [supp] [project]

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

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

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

  • 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] [project] [Data_360(code:bf28)] [Data_baidu]] [View_360(code:515a)] [View_baidu]]

  • Dataset-PTB: Shuran Song and Jianxiong Xiao. "Tracking Revisited using RGBD Camera: Unified Benchmark and Baselines." ICCV (2013) [paper] [project] [5 validation] [95 evaluation]

  • 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] [project] Mirror Link:ALOV300++ Dataset Mirror Link:ALOV300++ Groundtruth

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

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

  • Dataset-VOT: [project]

[VOT13_paper_ICCV]The Visual Object Tracking VOT2013 challenge results

[VOT14_paper_ECCV]The Visual Object Tracking VOT2014 challenge results

[VOT15_paper_ICCV]The Visual Object Tracking VOT2015 challenge results

[VOT16_paper_ECCV]The Visual Object Tracking VOT2016 challenge results

[VOT17_paper_ICCV]The Visual Object Tracking VOT2017 challenge results

Distinguished Researchers & Teams

Distinguished visual tracking researchers who have published +3 papers which have a major impact on the field of visual tracking and are still active in the field of visual tracking.(Names listed in no particular order, I will continue to supplement this part.)

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visual tracker benchmark results