Kourosh Meshgi

Research Interests & Studies

 

Research > Tracking

Object tracking has attracted recent attention because of high demands for its everyday-life applications. Handling occlusions especially in cluttered environments introduced new challenges to the tracking problem. Depth-maps provide necessary clues to retrieve occluded objects after they reappear, recombine split group of objects, compensate drastic appearance changes, and reduce the effect of appearance artifacts. This page is dedicated to gather resources about RGBD tracking, as well as showing my research directions and my code repositry explaination in GitHub.

 

Research Community

Nov 11, 2013
 

Websites:

People:

Labs:

Videos:

Datasets:

Conferences:

  • AVSS (Advanced Video and Signal-based Survillience)
  • PETS (Privacy Enhancing Technology Symposium)
  • ICCV (International Conference on Computer Vision)
  • ECCV (European Conference on Computer Vision)

 

Influential Papers

May 28, 2014
 

Papers:

 

 

 

Histogram Distances:

Features Extraction and Testing:

  • HOG
  • SIFT
  • SURF

RGB Tracking:

 

NAME CODE REFERENCE
CPF CPF
(2002 ECCV) Color-Based Probabilistic Tracking, Perez et al.
KMS KMS
(2003 PAMI) Kernel-Based Object Tracking, Comaniciu et al.
SMS SMS
(2003 CVPR) Mean-shift Blob Tracking Through Scale Space, Collins
VR-V VIVID/VR
(2005 PAMI) Online Selection of Discriminative Tracking Features, Collins et al.
Frag Frag
(2006 CVPR) Robust Fragments-based Tracking using the Integral Histogram, Adam et al.
OAB OAB
(2006 BMVC) Real-Time Tracking via On-line Boosting, Grabner et al.
IVT IVT
(2008 IJCV) Incremental Learning for Robust Visual Tracking, Ross et al.
SemiT SBT
(2008 ECCV) Semi-supervised On-Line Boosting for Robust Tracking, Grabner et al.
MIL MIL
(2009 CVPR) Visual Tracking with Online Multiple Instance Learning, Babenko et al.
BSBT BSBT
(2009 ICCV Wshop) Beyond Semi-Supervised Tracking: Tracking Should Be as Simple as Detection, but not Simpler than Recognition. Stalder et al.
TLD TLD
(2010 CVPR) P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints, Kalal et al.
VTD –
(2010 CVPR) Visual Tracking Decomposition, Kwon aand Lee
CXT CXT
(2011 CVPR) Context Tracker: Exploring supporters and distracters in unconstrained environments, Dinh et al.
LSK LSK
(2011 CVPR) Robust Tracking using Local Sparse Appearance Model and K-Selection, Liu
Struck Struck
(2011 ICCV)Struck: Structured Output Tracking with Kernels, Hare at al.
VTS –
(2011 ICCV) Tracking by Sampling Trackers, Kwon and Lee
ASLA ASLA
(2012 CVPR) Visual Tracking via Adaptive Structural Local Sparse Appearance Model, Jia et al.
DFT DFT
(2012 CVPR) Distribution Fields for Tracking, Sevilla-Lara and Learned-Miller
L1APG L1APG
(2012 CVPR) Real Time Robust L1 Tracker Using Accelerated Proximal Gradient Approach, Bao et al.
LOT LOT
(2012 CVPR) Locally Orderless Tracking, Oron et al.
MTT MTT
(2012 CVPR) Robust Visual Tracking via Multi-task Sparse Learning, Zhang et al.
ORIA ORIA
(2012 CVPR) Online Robust Image Alignment via Iterative Convex Optimization, Wu at el.
SCM SCM
(2012 CVPR)W. Zhong, H. Lu, and M.-H. Yang. Robust Object Tracking via Sparsity-based Collaborative Model, Zhong et al.
CSK CSK
(2012 ECCV) Exploiting the Circulant Structure of Tracking-by-Detection with Kernels, Henriques et al.
CT CT
(2012 ECCV) Real-time Compressive Tracking, Zhang et al.

 

RGBD Tracking:

Surveys:

Reading Lists:

 

Books:

 

Code Snippets and Projects

Nov 11, 2013
 

Open Source & Reasearch Projects:

Useful Codes:

Commercial Projects:

 

Publication

May 28, 2014
 

Paper:

Code:

  • The Online Open Source Repository of my Developing Tool for RGB-D Tracking is Placed in GitHub...
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