Multi images retrieved for automatic intruder detection using kernel-based learning algorithm
Abstract
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.