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.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/88
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.
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.com
dc.identifier.otherhttps://dspace.bracu.ac.bd/server/api/core/items/208ba06e-24fb-460d-917f-b3f5f6e6ef8c
dc.identifier.urihttp://hdl.handle.net/10361/7206
dc.language.isoen
dc.publisher© 2012 International Journal of Communication Networks and Information Security
dc.sourceBRAC University Institutional Repository
dc.subjectAutomated surveillance
dc.subjectLearning algorithms
dc.subjectOnline anomaly detection
dc.subjectReal-time outlier detection
dc.titleAutomated intruder detection from image sequences using minimum volume sets
dc.typeArticle

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