Ahmed, Supriyo ShafkatShahrear, S.M FardinKarim, Kazi Lamiyah DarakshaMuntaha, RakibunAnika, Afra2016-09-212016-09-2120162016-08ID 12110021ID 12110020ID 12110031ID 12110019http://hdl.handle.net/10361/6433Cataloged from PDF version of thesis report.Includes bibliographical references (page 54-55).This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2016.Over the course of the last few decades, security systems have undergone radical overhauls and have transitioned into such sophisticated devices that some even completely overcome any form of human interventions. In this paper we apply a recursive Kernel-based Online Anomaly Detection algorithm to propose an automated, real-time intruder detection mechanism for surveillance networks. Our proposed method is portable and adaptive, and does not require any expensive or advanced components. Real images are collected using a rotating camera over a span of space and time, along with the comparison using common methods based on Principle Component Analysis (PCA), can show that it is possible to obtain high detection accuracy with low complexity. Thus, it could also exhibit sensitivity to threshold choices, and have no natural distributed form.55 pagesenBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Automatic intruderPrinciple Component Analysis (PCA)Kernel-based Online Anomaly Detection Algorithm (KOAD)Thesis