Design, implementation and performance analysis of pervasive surveillance networks

Abstract

Pervasive surveillance implies the continuous tracking of multiple targets as they move about the monitored region. The tasks to be performed by a surveillance system are expressed as the following requirements:(1) Automatically track the identified targets over the region being monitored: (2) Provide concise feedback and video data of a tracked target to multiple operators. The active sensors needed to track the target keep changing due to target motion. Hence in order to provide concise and relevant information to a human operator to assist in decision making, the video feedback provided to the operator needs to be switched to the sensors currently involved in the tracking task. Another important aspect of surveillance systems is the ability of track multiple targets simultaneously using sensors with motion capability. Current feature (point) based visual surveillance and tracking techniques generally employed do not provide an adequate framework to express the surveillance task of tracking multiple targets simultaneously using a single sensor. This paper presents a mutational analysis approach for shape based control to model a multi-target surveillance scenario. A surveillance testbed has been designed based on these requirements and the proposed algorithms and subsystems are implemented on it and a performance analysis of proposed methods have been provided. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.