Automated intruder detection from image sequences using minimum volume sets
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Date
2012
Journal Title
Journal ISSN
Volume Title
Publisher
© 2012 International Journal of Communication Networks and Information Security
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.
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
Keywords
Automated surveillance, Learning algorithms, Online anomaly detection, Real-time outlier detection
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