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    Automated intruder detection from image sequences using minimum volume sets

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    Date
    2012
    Publisher
    © 2012 International Journal of Communication Networks and Information Security
    Author
    Ahmed, Tarem
    Wei, Xianglin
    Ahmed, Supriyo Sabbir
    Pathan, Al-Sakib Khan
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10361/7206
    Citation
    Ahmed, T., Wei, X., Ahmed, S., & Pathan, A. K. (2012). Automated intruder detection from image sequences using minimum volume sets. International Journal of Communication Networks and Information Security, 4(1), 11-17. Retrieved from www.scopus.com
    Abstract
    We propose a new algorithm based on machine learning techniques for automatic intruder detection in visual surveillance networks. The proposed algorithm is theoretically founded on the concept of Minimum Volume Sets. Through application to image sequences from two different scenarios and comparison with existing algorithms, we show that it is possible for our proposed algorithm to easily obtain high detection accuracy with low false alarm rates.
    Keywords
    Automated surveillance; Learning algorithms; Online anomaly detection; Real-time outlier detection
     
    Description
    This article was published in the International Journal of Communication Networks and Information Security [© 2014 IJCNIS] and The Article's website is at: http://www.ijcnis.org/index.php/ijcnis/article/view/88
    Publisher Link
    http://www.ijcnis.org/index.php/ijcnis/article/view/88
    Department
    Department of Electrical and Electronic Engineering, BRAC University
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