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Automated intruder detection from image sequences using minimum volume sets

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dc.contributor.authorAhmed, Tarem
dc.contributor.authorWei, Xianglin
dc.contributor.authorAhmed, Supriyo Sabbir
dc.contributor.authorPathan, Al-Sakib Khan
dc.contributor.departmentDepartment of Electrical and Electronic Engineering
dc.date.accessioned2016-12-12T08:39:20Z
dc.date.available2016-12-12T08:39:20Z
dc.date.issued2012
dc.descriptionThis 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/88en_US
dc.description.abstractWe 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.en_US
dc.description.versionPublished
dc.identifier.citationAhmed, 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.comen_US
dc.identifier.issn20760930
dc.identifier.urihttp://hdl.handle.net/10361/7206
dc.language.isoenen_US
dc.publisher© 2012 International Journal of Communication Networks and Information Securityen_US
dc.relation.urihttp://www.ijcnis.org/index.php/ijcnis/article/view/88
dc.subjectAutomated surveillanceen_US
dc.subjectLearning algorithmsen_US
dc.subjectOnline anomaly detectionen_US
dc.subjectReal-time outlier detectionen_US
dc.titleAutomated intruder detection from image sequences using minimum volume setsen_US
dc.typeArticleen_US

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Faculty Publications, Department of Electrical and Electronic Engineering (EEE)