Conference Paper

Permanent URI for this collectionhttps://hdl.handle.net/10361/6902

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    Adaptive algorithms for automated intruder detection in surveillance networks
    (© 2014 Institute of Electrical and Electronics Engineers Inc., 2014) Ahmed, Tarem; Ahmed, Supriyo Sabbir; Pathan, Al-Sakib Khan; Department of Electrical and Electronic Engineering
    Many types of automated visual surveillance systems have been presented in the recent literature. Most of the schemes require custom equipment, or involve significant complexity and storage needs. After studying the area in detail, this work presents four novel algorithms to perform automated, real-time intruder detection in surveillance networks. Built using machine learning techniques, the proposed algorithms are adaptive and portable, do not require any expensive or sophisticated component, are lightweight, and efficient with runtimes of the order of hundredths of a second. Two of the proposed algorithms have been developed by us. With application to two complementary data sets and quantitative performance comparisons with two representative existing schemes, we show that it is possible to easily obtain high detection accuracy with low false positives.
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    Taking meredith out of Grey's anatomy: automating hospital ICU emergency signaling
    (© 2016 Institute of Electrical and Electronics Engineers Inc., 2016-05) Ahmed, Tarem; Ahmed, Supriyo Shafkat; Chowdhury, Fazle Elahi; Department of Electrical and Electronic Engineering
    In this paper we propose a new algorithm based on kernel machines for automatic, instantaneous detection of emergencies occurring in a hospital Intensive Care Unit. The proposed algorithm takes as input the multitude of vital statistics that are continuously monitored for a critical patient in Intensive Care, learns the underlying pattern between the statistics that is naturally inherent for the particular patient, and instantaneously signals any deviation from this pattern. Through application to real data from a cardiac Intensive Care Unit at a hospital in a developing country, we show that it is possible to easily obtain high detection accuracy with low false alarm rates.