Resources in Visual Tracking

最新推荐文章于 2024-07-02 21:45:41 发布

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最新推荐文章于 2024-07-02 21:45:41 发布

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原文链接：http://www.cnblogs.com/lxy2017/p/4056231.html

本文汇总了视觉跟踪领域的多项研究成果，介绍了多个知名研究团队的主要贡献，并回顾了该领域的重要论文和算法，如TLD等。

这个应该是目前最全的Tracking相关的文章了

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

原文：http://blog.youkuaiyun.com/minstyrain/article/details/38640541

转载于:https://www.cnblogs.com/lxy2017/p/4056231.html