Resources in Visual Tracking

This should be the most comprehensive tracking-related article at present.

I. surveyand benchmark:

1. pami2014: visualtracking _ An experimental survey, code: http://alov300pp.joomlafree.it/trackers-resource.html

2. cvpr2013: Online Object Tracking: A benchmark (FQ required)

3. signalprocessing 2011: Video Tracking theory andpractice

4. accv2006: tutorials-advances in visualtracking: Chinese: Progress of Visual Tracking

5. evaluationof an online learning approach for robust Object Tracking

 

2. Research Groups:

1. universityof California at Merced

Publicationspami: 6, cvpr: 26, eccv: 17, bmcv: 6, Nips: 6, ijcv: 3, accv: 3

Masterpiece: 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 sor: kyoungmulee published 5 articles on PAMI in 2013.

Document list: 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, linghaibin

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 associatestmsor: 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. Lu huchuan, a professor at Dalian University of Technology, is the first in the field of tracking in China.

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 2 DPCA 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 lbfeatures

6. Professor at Nanjing Information Engineering University, Kaihua Zhang

7. oregonstatepolicsor, Sinisa Todorovic switched from video segmentation to tracking

CSL, Cvpr2014: Multi-objecttracking via Constrained sequential labeling

Cvpr2011: multiobjecttracking as maximum weight Independent Set

8. grazuniversity of technology, Austria, Horst possegger, PhD

Cvpr2014: occlusiongeodesics for online multi-object tracking

Cvpr2013: robustreal-time tracking of multiple objects by volumetric mass Densities

9. Zdenek kalal, PhD, University of Maryland

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

Explanation:

TLD Visual Tracking Algorithm

TLD source code deep analysis

Ding jieniu TLD

TLD (tracking-Learning-detection) learning and source code understanding

 

Iii. Other early work:

Tracking of a non-rigid objectviapatch-based Dynamic Appearance Modeling and adaptive basin hopping Monte carlosampling

Tracking-by-detection

Particle Filter demonstration and opencv code

Opencv Study Notes-Getting Started (6)-camshift

Principle of camshift Algorithm and Its opencv implementation

Camshift Algorithm

Camshift algorithm, opencv Implementation 1 -- Back Projection

Objective tracking study note _ 2 (particle filter study 1)

Objective tracking study note _ 3 (particle filter study 2)

Objective tracking study note 4 (particle filter 3)

Target Tracking learning Series 1: On-line boosting and vision reading

Original article: http://blog.csdn.net/minstyrain/article/details/38640541

Resources in Visual Tracking