Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Automatic object detection based on adaptive background subtraction using symmetric alpha stable distribution
AU - Bhaskar, H.
AU - Mihaylova, L.
AU - Maskell, S.
N1 - pp. 197-203 Printed by the Institution of Engineering and Technology, London, ISBN 9780863419102 ISSN 0537-9989 Reference PES08273
PY - 2008/4/15
Y1 - 2008/4/15
N2 - Automatic detection of objects is critical to video tracking systems. One of the simplest techniques for detection is background subtraction (BS). BS refers to the process of segmenting moving regions from image sequences. The BS process involves building a model of the background and extracting regions of the foreground (moving objects). In this paper, we propose an extended cluster BS (CBS) technique based on symmetric alpha stable (SAS) distributions. The developed method functions at cluster-level as against the traditional pixel-level BS methods. An iterative self-adaptive mechanism is presented that allows automated learning of the distribution of the model parameters. The results for the CBS S®S algorithm on real video sequences show improvement compared with a CBS using a Gaussian mixture model.
AB - Automatic detection of objects is critical to video tracking systems. One of the simplest techniques for detection is background subtraction (BS). BS refers to the process of segmenting moving regions from image sequences. The BS process involves building a model of the background and extracting regions of the foreground (moving objects). In this paper, we propose an extended cluster BS (CBS) technique based on symmetric alpha stable (SAS) distributions. The developed method functions at cluster-level as against the traditional pixel-level BS methods. An iterative self-adaptive mechanism is presented that allows automated learning of the distribution of the model parameters. The results for the CBS S®S algorithm on real video sequences show improvement compared with a CBS using a Gaussian mixture model.
KW - automatic object detection
KW - tracking
KW - background subtraction
KW - alpha stable distribution
KW - video sequences DCS-publications-id
KW - inproc-562
KW - DCS-publications-credits
KW - dsp-fa
KW - DCS-publications-personnel-id
KW - 121
KW - 132
M3 - Conference contribution/Paper
SN - 978-0-86341-910-2
SP - 197
EP - 203
BT - Target Tracking and Data Fusion: Algorithms and Applications, 2008 IET Seminar on
T2 - The Institution of Engineering and Technology Seminar on Target Tracking and Data Fusion: Algorithms and Applications
Y2 - 15 April 2008 through 16 April 2008
ER -