传统的跟踪算法大多从物体的外观出发，只能在线学习，从当前的视频中在线抓取数据进行学习跟踪的算法，如：TLD、Struck、KCF，这类算法必须足够简单才行，否则耗时严重。当然现在也有人使用卷积网络进行离线训练，在线跟踪，但是当跟踪目标未知时，需要利用随机梯度下降法(SGD)在线微调网络权重，从而使得速度下降，做不到实时跟踪。

综上，浅层学习方式，如相关滤波，利用网络内部参数作为特征，这样不能充分发挥“端对端”的优势。而利用SGD微调多层网络参数的方式无法实时跟踪。

    近期的研究工作有：

1.利用RNN网络进行训练，通过预测目标在各帧中的位置来跟踪，同时加入了可区分的“注意力机制”。虽然该方法目前无法在现有的标准测试集上取得显著的结果，但是有足够的潜力；

2.利用粒子滤波的方式，通过训练好的距离矩阵比较当前帧与第一帧的区别，其中距离矩阵是利用了首先玻尔兹曼机（RBM）和随机点的方式训练所得。此方法与本文中的方法差异太大，因此没有使用该方法。

3.离线预训练+在线微调方式，其中SO-DLT和MDNet均离线训练了一个相似性检测的卷积网络，并在线使用SGD算法进行微调。利用这种方法的Deep SRDCF和 FCNT均取得了很好的结果，但是在速度上依旧不行。

4.GOTURN算法也采用了YCNN的结构，但是该算法无法控制下一帧的变换形式，不具有变换的内在不变性，除非样本集包含所有种类所有位置的变换。并且不能自适应调节搜索区域的大小。

5.SINT（Siamese Instance search Tracker）算法从名字上看像是从Instance级别上去搜索，它采用非全卷积的结构，在图像中均匀分布着类似Struck算法中的圆形区域，然后利用光流和标记框的修正来提升效果，并通过ROI区域来提升速度，最终达到了2fps。

这里介绍下， 如果搭建基于matlab的深度学习神经网络的GPU仿真搭建。

1.版本组合：Win7+Matlab R2015b+CUDA7.5+vs2013

CUDA7.5下载地址为：http://developer.download.nvidia.com/compute/cuda/7.5/Prod

/local_installers/cuda_7.5.18_windows.exe

VS2013要专业版。

如下所示：

全部安装好之后，做如下操作：

2.CPU配置，运行CNN工具箱中的vl_setupnn.m，然后再运行vl_compilenn.m可以完成CPP文件的编译。

3.编译成功后，会产生

这些必须在电脑上编译，否则别人的复制给你，如果配置不一样，可能会报错。

4.GPU配置：

安装cudnn：https://developer.nvidia.com/rdp/cudnn-archive，放到CNN工具箱中的新建local文件夹中，

然后mex -setup下，操作和CPU一样。

然后执行matlab程序：

vl_setupnn;

vl_compilenn('enableGpu', true,'cudaRoot', 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.5','cudaMethod', 'nvcc', 'enableCudnn', 'true','cudnnRoot',

'E:\A_2016_FPGA_Test\FC_tracking\A_FC\matconvnet-1.0-beta20\local\cudnn');

红色部分为自己的安装路径，不要有中文。

目标跟踪相关资源：

Visual Trackers

• ECO: Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg. "ECO: Efficient Convolution Operators for Tracking." CVPR (2017). [paper] [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] [project] [github]
• CACF: Matthias Mueller, Neil Smith, Bernard Ghanem. "Context-Aware Correlation Filter Tracking." CVPR (2017 oral). [paper] [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] [project] [code]
• MCPF: Tianzhu Zhang, Changsheng Xu, Ming-Hsuan Yang. "Multi-task Correlation Particle Filter for Robust Visual 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] [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). [paper] [project]
• CSR-DCF: Alan Lukežič, Tomáš Vojíř, Luka Čehovin, Jiří Matas, Matej Kristan. "Discriminative Correlation Filter with Channel and Spatial Reliability." CVPR (2017). [paper]
• BACF: Hamed Kiani Galoogahi, Ashton Fagg, Simon Lucey. "Learning Background-Aware Correlation Filters for Visual Tracking." CVPR (2017). [paper]
• Bohyung Han, Jack Sim, Hartwig Adam "BranchOut: Regularization for Online Ensemble Tracking with Convolutional Neural Networks." CVPR (2017).
• SANet: Heng Fan, Haibin Ling. "SANet: Structure-Aware Network for Visual Tracking." CVPRW (2017). [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]
• 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]
• 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]
• 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]
• CNT: Kaihua Zhang, Qingshan Liu, Yi Wu, Minghsuan Yang. "Robust Visual Tracking via Convolutional Networks Without Training." TIP (2016). [paper] [code]
• DeepSRDCF: Martin Danelljan, Gustav Häger, Fahad Khan, Michael Felsberg. "Convolutional Features for Correlation Filter Based Visual Tracking." ICCV workshop (2015). [paper] [project]
• SRDCF: Martin Danelljan, Gustav Häger, Fahad Khan, Michael Felsberg. "Learning Spatially Regularized Correlation Filters for Visual Tracking." ICCV (2015). [paper] [project]
• 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]
• CF2: Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang. "Hierarchical Convolutional Features for Visual Tracking." ICCV (2015) [paper] [project] [github]
• FCNT: Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu. "Visual Tracking with Fully Convolutional Networks." ICCV (2015). [paper] [project] [github]
• LCT: Chao Ma, Xiaokang Yang, Chongyang Zhang, Ming-Hsuan Yang. "Long-term Correlation Tracking." CVPR (2015). [paper] [project] [github]
• RPT: Yang Li, Jianke Zhu and Steven C.H. Hoi. "Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches." CVPR (2015). [paper] [github]
• 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]
• DSST: Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan and Michael Felsberg. "Accurate Scale Estimation for Robust Visual Tracking." BMVC (2014). [paper] [PAMI] [project]
• 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]
• KCF: João F. Henriques, Rui Caseiro, Pedro Martins, Jorge Batista. "High-Speed Tracking with Kernelized Correlation Filters." TPAMI (2015). [paper] [project]

Others

• Re3: Daniel Gordon, Ali Farhadi, Dieter Fox. "Re3 : Real-Time Recurrent Regression Networks for Object Tracking." arXiv (2017). [paper] [code]
• DCFNet: Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu. "Modeling and Propagating CNNs in a Tree Structure 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]
• DMSRDCF: Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg. "Deep Motion Features for Visual Tracking." ICPR Best Paper (2016). [paper]
• 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]
• Learnet: Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip H. S. Torr, Andrea Vedaldi. "Learning feed-forward one-shot learners." NIPS (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]
• Yao Sui, Ziming Zhang, Guanghui Wang, Yafei Tang, Li Zhang. "Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning." ECCV (2016). [paper] [project]
• Yao Sui, Guanghui Wang, Yafei Tang, Li Zhang. "Tracking Completion." ECCV (2016). [paper] [project]
• EBT: Gao Zhu, Fatih Porikli, and Hongdong Li. "Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals." CVPR (2016). [paper] [exe]
• RATM: Samira Ebrahimi Kahou, Vincent Michalski, Roland Memisevic. "RATM: Recurrent Attentive Tracking Model." arXiv (2015). [paper] [github]
• DAT: Horst Possegger, Thomas Mauthner, and Horst Bischof. "In Defense of Color-based Model-free Tracking." CVPR (2015). [paper] [project] [code]
• 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]
• SO-DLT: Naiyan Wang, Siyi Li, Abhinav Gupta, Dit-Yan Yeung. "Transferring Rich Feature Hierarchies for Robust Visual Tracking." arXiv (2015). [paper] [code]
• DLT: Naiyan Wang and Dit-Yan Yeung. "Learning A Deep Compact Image Representation for Visual Tracking." NIPS (2013). [paper] [project] [code]
• Naiyan Wang, Jianping Shi, Dit-Yan Yeung and Jiaya Jia. "Understanding and Diagnosing Visual Tracking Systems." ICCV (2015). [paper] [project] [code]
• Dataset-MoBe2016: Luka Čehovin, Alan Lukežič, Aleš Leonardis, Matej Kristan. "Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking." arXiv (2016). [paper]
• 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
• 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-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

联系：highspeedlogic

QQ ：1224848052

微信：HuangL1121

邮箱：1224848052@qq.com

网站：http://www.mat7lab.com/

网站：http://www.hslogic.com/

微信扫一扫：