Home > Research > Publications & Outputs > Articulated Human Body Parts Detection Based on...

Research

Associated organisational units

Electronic data

Links

Text available via DOI:

Keywords

View graph of relations

Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching. / Bhaskar, Harish; Mihaylova, Lyudmila; Maskell, S.
In: Neurocomputing, Vol. 100, No. 1, 16.01.2013, p. 58-73.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Bhaskar, H, Mihaylova, L & Maskell, S 2013, 'Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching', Neurocomputing, vol. 100, no. 1, pp. 58-73. https://doi.org/10.1016/j.neucom.2011.12.039

APA

Bhaskar, H., Mihaylova, L., & Maskell, S. (2013). Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching. Neurocomputing, 100(1), 58-73. https://doi.org/10.1016/j.neucom.2011.12.039

Vancouver

Bhaskar H, Mihaylova L, Maskell S. Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching. Neurocomputing. 2013 Jan 16;100(1):58-73. Epub 2012 May 11. doi: 10.1016/j.neucom.2011.12.039

Author

Bhaskar, Harish ; Mihaylova, Lyudmila ; Maskell, S. / Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching. In: Neurocomputing. 2013 ; Vol. 100, No. 1. pp. 58-73.

Bibtex

@article{28a86b4943964b12b8aeea78aac8732a,
title = "Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching",
abstract = "Detecting people or other articulated objects and localizing their body parts is a challenging computer vision problem as their movement is unpredictable under circumstances of partial and full occlusions. In this paper, a framework for human body parts tracking in video sequences using a self-adaptive clus- ter background subtraction (CBS) scheme is proposed based on a Gaussian mixture model (GMM) and foreground matching with rectangular pictorial structures. The efficiency of the designed human body parts tracking frame- work is illustrated over various real-world video sequences.",
keywords = "target tracking, background subtraction, optimisation, genetic algorithms, pictorial structures, articulated objects",
author = "Harish Bhaskar and Lyudmila Mihaylova and S. Maskell",
year = "2013",
month = jan,
day = "16",
doi = "10.1016/j.neucom.2011.12.039",
language = "English",
volume = "100",
pages = "58--73",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Articulated Human Body Parts Detection Based on Cluster Background Subtraction and Foreground Matching

AU - Bhaskar, Harish

AU - Mihaylova, Lyudmila

AU - Maskell, S.

PY - 2013/1/16

Y1 - 2013/1/16

N2 - Detecting people or other articulated objects and localizing their body parts is a challenging computer vision problem as their movement is unpredictable under circumstances of partial and full occlusions. In this paper, a framework for human body parts tracking in video sequences using a self-adaptive clus- ter background subtraction (CBS) scheme is proposed based on a Gaussian mixture model (GMM) and foreground matching with rectangular pictorial structures. The efficiency of the designed human body parts tracking frame- work is illustrated over various real-world video sequences.

AB - Detecting people or other articulated objects and localizing their body parts is a challenging computer vision problem as their movement is unpredictable under circumstances of partial and full occlusions. In this paper, a framework for human body parts tracking in video sequences using a self-adaptive clus- ter background subtraction (CBS) scheme is proposed based on a Gaussian mixture model (GMM) and foreground matching with rectangular pictorial structures. The efficiency of the designed human body parts tracking frame- work is illustrated over various real-world video sequences.

KW - target tracking

KW - background subtraction

KW - optimisation

KW - genetic algorithms

KW - pictorial structures

KW - articulated objects

U2 - 10.1016/j.neucom.2011.12.039

DO - 10.1016/j.neucom.2011.12.039

M3 - Journal article

VL - 100

SP - 58

EP - 73

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

IS - 1

ER -