relation: https://khub.utp.edu.my/scholars/17820/
title: Optimizing Visual Sensors Placement with Risk Maps Using Dynamic Programming
creator: Altahir, A.A.
creator: Asirvadam, V.S.
creator: Sebastian, P.
creator: Hamid, N.H.B.
creator: Ahmed, E.F.
description: Typically, optimizing the poses and placement of surveillance cameras is usually formulated as a discrete combinatorial optimization problem. The traditional aspects of solving the camera placement problem attempt to maximize the area monitored by the camera array and/or reduce the cost of installing a set of surveillance cameras. Several approximate optimization techniques have been proposed to locate near-optimal solution to the placement problem. Thus, related surveillance planning methods optimize the placement of visual sensors based on equally significance grids by not limiting to demand of coverage. This article explores the efficiency of the visual sensor placement based on a combination of two methods namely, a deterministic risk estimation for the risk assessment and a dynamic programming for optimizing the placement of surveillance cameras. That is, the enhanced efficiency of coverage is obtained by developing a prior grid assessment practice to stress on the security sensitive zones. Then, the dynamic programming algorithm operates on security quantified maps rather than uniform grids. The attained result is compared to the respective heuristic search algorithm outcomes. The overall assessment shows the reliability of the proposed methods' combinations. © 2001-2012 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Article
type: PeerReviewed
identifier:   Altahir, A.A. and Asirvadam, V.S. and Sebastian, P. and Hamid, N.H.B. and Ahmed, E.F.  (2022) Optimizing Visual Sensors Placement with Risk Maps Using Dynamic Programming.  IEEE Sensors Journal, 22 (1).  pp. 393-404.  ISSN 1530437X     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120043858&doi=10.1109%2fJSEN.2021.3127989&partnerID=40&md5=8846d1ebd5eb88e4ccef76d851f9877b
relation: 10.1109/JSEN.2021.3127989
identifier: 10.1109/JSEN.2021.3127989