视频跟踪领域总结全面



一、Surveyand benchmark：

1. PAMI2014：VisualTracking_
An Experimental Survey，代码：http://alov300pp.joomlafree.it/trackers-resource.html
2. CVPR2013:Online Object Tracking: A Benchmark(需翻墙)
3. SignalProcessing 2011：Video Tracking Theory andPractice
4. ACCV2006：Tutorials-Advances
in VisualTracking：中文：视觉跟踪的进展
5. Evaluationof an online learning approach for robust object tracking

二、研究团体：
1. Universityof California at Merced：Ming-HsuanYang视觉跟踪当之无愧第一人，后面的人基本上都和气其有合作关系，他引近9000
PublicationsPAMI：6，CVPR：26，ECCV：17，BMCV：6，NIPS：6，IJCV：3，ACCV：3
代表作：RobustVisual Tracking via Consistent Low-Rank Sparse Learning
FCT,IJCV2014:FastCompressive Tracking
RST,PAMI2014:RobustSuperpixel Tracking; SPT,ICCV2011, Superpixeltracking
SVD,TIP2014:LearningStructured Visual Dictionary for Object Tracking
ECCV2014: SpatiotemporalBackground Subtraction Using Minimum Spanning Tree and Optical Flow
PAMI2011:RobustObject Tracking with Online Multiple Instance Learning
MIT,CVPR2009: Visualtracking with online multiple instance learning
IJCV2008: IncrementalLearning for Robust Visual Tracking

2. SeoulNational University Professor：KyoungMuLee2013年在PAMI上发表5篇，至今无人能及
文献列表PAMI:13,CVPR:30,ECCV:12,ICCV:8,PR:4
PAMI2014：A GeometricParticle Filter for Template-Based Visual Tracking
ECCV2014: Robust Visual Tracking with Double Bounding Box Model
PAMI2013:HighlyNonrigid Object Tracking via Patch-based Dynamic Appearance Modeling
CVPR2014: Interval Tracker: Tracking by Interval Analysis
CVPR2013: MinimumUncertainty Gap for Robust Visual Tracking
CVPR2012:RobustVisual Tracking using Autoregressive Hidden Markov Model
VTS,ICCV2011:Tracking by Sampling Trackers.
VTD,CVPR2010: VisualTracking Decomposition
TST,ICCV2011:Tracking by sampling trackers
3. TempleUniversity，凌海滨
Publication List PMAI:4,CVPR:19,ICCV:17,ECCV:5,TIP:9
CVPR2014:Multi-targetTracking with Motion Context in Tenor Power Iteration
ECCV2014:TransferLearning Based Visual Tracking with Gaussian Process Regression
ICCV2013:Findingthe Best from the Second Bests - Inhibiting Subjective Bias in Evaluation ofVisual Tracking Algorithms
CVPR2013: Multi-targetTracking by Rank-1 Tensor Approximation
CVPR2012:RealTime Robust L1 Tracker Using Accelerated Proximal Gradient Approach
TIP2012: Real-timeProbabilistic Covariance Tracking with Efficient Model Update
ICCV2011: BlurredTarget Tracking by Blur-driven Tracker
PAMI2011ICCV2009: RobustVisual Tracking and Vehicle Classification via Sparse Representation
ICCV2011:RobustVisual Tracking using L1 Minimization
L1O,CVPR2011: Minimumerror bounded efficient l1 tracker with occlusion detection
L1T, ICCV2009:Robustvisual tracking using l1 minimization
4. HongKong Polytechnic University AssociateProfessor: Lei Zhang
PapersPAMI:2,CVPR:18,ICCV:14,ECCV:12,ICPR:6,PR:28,TIP:4
STC,ECCV2014: FastTracking via Dense Spatio-Temporal Context Learning
FCT,PAMI2014,ECCV2012:Fast CompressiveTracking, Minghsuan Yang
IETComputer Vision2012:Scale and Orientation Adaptive Mean Shift Tracking
IJPRAI2009:RobustObject Tracking using Joint Color-Texture Histogram
5. 大连理工大学教授 卢湖川国内追踪领域第一人
CVPR2014:VisualTracking via Probability Continuous Outlier Model
TIP2014:VisualTracking via Discriminative Sparse Similarity Map
TIP2014: RobustSuperpixel Tracking
TIP2014: RobustObject Tracking via Sparse Collaborative Appearance Model
CVPR2013: LeastSoft-threshold Squares Tracking, MinghsuanYang
TIP2013:Online Object Trackingwith Sparse Prototypes, Minghsuan Yang
SignalProcessing Letters2013: Graph-RegularizedSaliency Detection With Convex-Hull-Based Center Prior
SignalProcessing2013: On-line LearningParts-based Representation via Incremental Orthogonal Projective Non-negativeMatrix Factorization
CVPR2012:RobustObject Tracking viaSparsity-based Collaborative Model, MinghsuanYang
CVPR2012:VisualTracking via Adaptive Structural Local Sparse Appearance Model,
MinghsuanYang
SignalProcessing Letters 2012:Object tracking via 2DPCA and L1-regularization
IETImage Processing 2012:Visual Tracking via Bag of Features
ICPR2012:Superpixel Level Object Recognition Under Local Learning Framework
ICPR2012: Fragment-BasedTracking Using Online Multiple Kernel Learning
ICPR2012: ObjectTracking Based On Local Learning
ICPR2012: ObjectTracking with L2_RLS
ICPR2011:ComplementaryVisual Tracking
FG2011:OnlineMultiple Support Instance Tracking
SignalProcessing2010: A novel methodfor gaze tracking by local pattern model and support vector regressor
ACCV2010: OnFeature Combination and Multiple Kernel Learning for Object Tracking
ACCV: RobustTracking Based on Pixel-wise Spatial Pyramid and Biased Fusion
ACCV2010: HumanTracking by Multiple Kernel Boosting with Locality Affinity Constraints
ICCV2011:SuperpixelTracking, Minghsuan Yang
ICPR2010: RobustTracking Based on Boosted Color Soft Segmentation and ICA-R
ICPR2010: IncrementalMPCA for Color Object Tracking
ICPR2010: Bagof Features Tracking
ICPR2008: GazeTracking By Binocular Vision and LBP Features
6. 南京信息工程大学教授,KaiHua Zhang
7. OregonstateProfessor,Sinisa Todorovic由视频分割转向Tracking
CSL,CVPR2014: Multi-ObjectTracking via Constrained Sequential Labeling
CVPR2011:MultiobjectTracking as Maximum Weight Independent Set
8. GrazUniversity of Technology, Austria，Horst Possegger博士
CVPR2014:OcclusionGeodesics for Online Multi-Object Tracking
CVPR2013: RobustReal-Time Tracking of Multiple Objects by Volumetric Mass Densities
9. 马里兰大学Zdenek Kalal博士
TLD,PAMI2011: Tracking-Learning-Detection
TIP2010: Face-TLD:Tracking-Learning-Detection Applied to Faces
ICPR2010:Forward-BackwardError: Automatic Detection of Tracking Failures
CVPR2010: P-N Learning:Bootstrapping Binary Classifiers by Structural Constraints
BMVC2008: Weighted Sampling forLarge-Scale Boosting
中文讲解：
TLD视觉跟踪算法
TLD源码深度分析
庖丁解牛TLD
TLD（Tracking-Learning-Detection）学习与源码理解

三、其他早期工作：
Tracking of a Non-Rigid ObjectviaPatch-based Dynamic Appearance Modeling and Adaptive Basin Hopping Monte CarloSampling
tracking-by-detection
粒子滤波演示与opencv代码
opencv学习笔记-入门（6）-camshift
Camshift算法原理及其Opencv实现
Camshift算法
CamShift算法，OpenCV实现1--Back
Projection
目标跟踪学习笔记_2(particle
filter初探1)
目标跟踪学习笔记_3(particle
filter初探2)
目标跟踪学习笔记_4(particle
filter初探3)
目标跟踪学习系列一:on-line boosting and vision 阅读

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