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Automatic object detection based on adaptive background subtraction using symmetric alpha stable distribution

Bhaskar, H. and Mihaylova, L. and Maskell, S. (2008) Automatic object detection based on adaptive background subtraction using symmetric alpha stable distribution. In: Target Tracking and Data Fusion: Algorithms and Applications, 2008 IET Seminar on. , 197 - 203. ISBN 978-0-86341-910-2

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    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.

    Item Type: Contribution in Book/Report/Proceedings
    Additional Information: pp. 197-203 Printed by the Institution of Engineering and Technology, London, ISBN 9780863419102 ISSN 0537-9989 Reference PES08273
    Uncontrolled Keywords: automatic object detection ; tracking ; background subtraction ; alpha stable distribution ; video sequences DCS-publications-id ; inproc-562 ; DCS-publications-credits ; dsp-fa ; DCS-publications-personnel-id ; 121 ; 132
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 8321
    Deposited By: Dr L Mihaylov
    Deposited On: 18 Apr 2008 08:54
    Refereed?: No
    Published?: Published
    Last Modified: 25 Feb 2018 02:44
    Identification Number:

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