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A canonical correlation analysis based motion model for probabilistic visual tracking

Tom Heyman, Vincent Spruyt, Sebastian Grünwedel, Alessandro Ledda UGent and Wilfried Philips UGent (2012) 2012 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP). p.HO2-25
abstract
Particle filters are often used for tracking objects within a scene. As the prediction model of a particle filter is often implemented using basic movement predictions such as random walk, constant velocity or acceleration, these models will usually be incorrect. Therefore, this paper proposes a new approach, based on a Canonical Correlation Analysis (CCA) tracking method which provides an object specific motion model. This model is used to construct a proposal distribution of the prediction model which predicts new states, increasing the robustness of the particle filter. Results confirm an increase in accuracy compared to state-of-the-art methods
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
particle filter, prediction model, Canonical Correlation Analysis, Object tracking
in
2012 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
pages
HO2 - 25
publisher
Proceedings of Visual Communications and Image Processing
place of publication
San Diego, California
conference name
IEEE Visual Communications and Image Processing (VCIP)
conference location
San Diego, California
conference start
2012-11-27
conference end
2012-11-30
Web of Science type
Proceedings Paper
Web of Science id
000315440800075
ISBN
9781467344050
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
4158504
handle
http://hdl.handle.net/1854/LU-4158504
date created
2013-10-09 08:45:52
date last changed
2017-01-02 09:52:18
@inproceedings{4158504,
  abstract     = {Particle \unmatched{fb01}lters are often used for tracking objects within a scene. As the prediction model of a particle \unmatched{fb01}lter is often implemented using basic movement predictions such as random walk, constant velocity or acceleration, these models will usually be incorrect. Therefore, this paper proposes a new approach, based on a Canonical Correlation Analysis (CCA) tracking method which provides an object speci\unmatched{fb01}c motion model. This model is used to construct a proposal distribution of the prediction model which predicts new states, increasing the robustness of the particle \unmatched{fb01}lter. Results con\unmatched{fb01}rm an increase in accuracy compared to state-of-the-art methods},
  author       = {Heyman, Tom and Spruyt, Vincent and Gr{\"u}nwedel, Sebastian and Ledda, Alessandro and Philips, Wilfried},
  booktitle    = {2012 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)},
  isbn         = {9781467344050},
  keyword      = {particle filter,prediction model,Canonical Correlation Analysis,Object tracking},
  language     = {eng},
  location     = {San Diego, California},
  pages        = {HO2--25},
  publisher    = {Proceedings of Visual Communications and Image Processing},
  title        = {A canonical correlation analysis based motion model for probabilistic visual tracking},
  year         = {2012},
}

Chicago
Heyman, Tom, Vincent Spruyt, Sebastian Grünwedel, Alessandro Ledda, and Wilfried Philips. 2012. “A Canonical Correlation Analysis Based Motion Model for Probabilistic Visual Tracking.” In 2012 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), HO2–25. San Diego, California: Proceedings of Visual Communications and Image Processing.
APA
Heyman, T., Spruyt, V., Grünwedel, S., Ledda, A., & Philips, W. (2012). A canonical correlation analysis based motion model for probabilistic visual tracking. 2012 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP) (pp. HO2–25). Presented at the IEEE Visual Communications and Image Processing (VCIP), San Diego, California: Proceedings of Visual Communications and Image Processing.
Vancouver
1.
Heyman T, Spruyt V, Grünwedel S, Ledda A, Philips W. A canonical correlation analysis based motion model for probabilistic visual tracking. 2012 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP). San Diego, California: Proceedings of Visual Communications and Image Processing; 2012. p. HO2–25.
MLA
Heyman, Tom, Vincent Spruyt, Sebastian Grünwedel, et al. “A Canonical Correlation Analysis Based Motion Model for Probabilistic Visual Tracking.” 2012 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP). San Diego, California: Proceedings of Visual Communications and Image Processing, 2012. HO2–25. Print.