Accelerated People Tracking using Texture in a Camera Network

Wasit Limprasert; Andrew Michael Wallace; Gregory Michaelson

doi:10.5220/0003813802250234

Accelerated People Tracking using Texture in a Camera Network

Wasit Limprasert, Andrew Michael Wallace, Gregory Michaelson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present an approach to tracking multiple human subjects within a camera network. A particle filter frame-work is used in which we combine foreground-background subtraction with a novel approach to texture learning and likelihood computation based on an ellipsoid model. As there are inevitable problems with multiple subjects due to occlusion and crossing, we include a robust method to suppress distraction between subjects. To achieve real-time performance, we have also developed our code on a graphics processing unit to achieve a 10-fold reduction in processing time with an approximate frame rate of 10 frames per second.

Original language	English
Title of host publication	Proceedings of the International Conference on Computer Vision Theory and Applications 2012
Publisher	SciTePress
Pages	225-234
Number of pages	10
Volume	1
ISBN (Print)	9789898565044
DOIs	https://doi.org/10.5220/0003813802250234
Publication status	Published - Feb 2012
Event	International Conference on Computer Vision Theory and Applications 2012 - Rome, Italy Duration: 24 Feb 2012 → 26 Feb 2012

Conference

Conference	International Conference on Computer Vision Theory and Applications 2012
Abbreviated title	VISAPP 2012
Country/Territory	Italy
City	Rome
Period	24/02/12 → 26/02/12

Access to Document

10.5220/0003813802250234Licence: CC BY-NC-ND

Cite this

@inproceedings{980ec09a380d4fd89d6d4d14f49716f2,

title = "Accelerated People Tracking using Texture in a Camera Network",

abstract = "We present an approach to tracking multiple human subjects within a camera network. A particle filter frame-work is used in which we combine foreground-background subtraction with a novel approach to texture learning and likelihood computation based on an ellipsoid model. As there are inevitable problems with multiple subjects due to occlusion and crossing, we include a robust method to suppress distraction between subjects. To achieve real-time performance, we have also developed our code on a graphics processing unit to achieve a 10-fold reduction in processing time with an approximate frame rate of 10 frames per second.",

author = "Wasit Limprasert and Wallace, \{Andrew Michael\} and Gregory Michaelson",

year = "2012",

month = feb,

doi = "10.5220/0003813802250234",

language = "English",

isbn = "9789898565044",

volume = "1",

pages = "225--234",

booktitle = "Proceedings of the International Conference on Computer Vision Theory and Applications 2012",

publisher = "SciTePress",

note = "International Conference on Computer Vision Theory and Applications 2012, VISAPP 2012 ; Conference date: 24-02-2012 Through 26-02-2012",

}

Limprasert, W, Wallace, AM & Michaelson, G 2012, Accelerated People Tracking using Texture in a Camera Network. in Proceedings of the International Conference on Computer Vision Theory and Applications 2012. vol. 1, SciTePress, pp. 225-234, International Conference on Computer Vision Theory and Applications 2012, Rome, Italy, 24/02/12. https://doi.org/10.5220/0003813802250234

TY - GEN

T1 - Accelerated People Tracking using Texture in a Camera Network

AU - Limprasert, Wasit

AU - Wallace, Andrew Michael

AU - Michaelson, Gregory

PY - 2012/2

Y1 - 2012/2

N2 - We present an approach to tracking multiple human subjects within a camera network. A particle filter frame-work is used in which we combine foreground-background subtraction with a novel approach to texture learning and likelihood computation based on an ellipsoid model. As there are inevitable problems with multiple subjects due to occlusion and crossing, we include a robust method to suppress distraction between subjects. To achieve real-time performance, we have also developed our code on a graphics processing unit to achieve a 10-fold reduction in processing time with an approximate frame rate of 10 frames per second.

AB - We present an approach to tracking multiple human subjects within a camera network. A particle filter frame-work is used in which we combine foreground-background subtraction with a novel approach to texture learning and likelihood computation based on an ellipsoid model. As there are inevitable problems with multiple subjects due to occlusion and crossing, we include a robust method to suppress distraction between subjects. To achieve real-time performance, we have also developed our code on a graphics processing unit to achieve a 10-fold reduction in processing time with an approximate frame rate of 10 frames per second.

UR - https://www.scopus.com/pages/publications/84862146896

U2 - 10.5220/0003813802250234

DO - 10.5220/0003813802250234

M3 - Conference contribution

SN - 9789898565044

VL - 1

SP - 225

EP - 234

BT - Proceedings of the International Conference on Computer Vision Theory and Applications 2012

PB - SciTePress

T2 - International Conference on Computer Vision Theory and Applications 2012

Y2 - 24 February 2012 through 26 February 2012

ER -

Accelerated People Tracking using Texture in a Camera Network

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this