Automated Camera Placement using Hybrid Particle Swarm Optimization
Context. Automatic placement of surveillance cameras' 3D models in an arbitrary floor plan containing obstacles is a challenging task. The problem becomes more complex when different types of region of interest (RoI) and minimum resolution are considered. An automatic camera placement decis...
Main Authors: | , |
---|---|
Format: | Others |
Language: | English |
Published: |
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik
2014
|
Subjects: | |
Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3326 |
id |
ndltd-UPSALLA1-oai-DiVA.org-bth-3326 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-UPSALLA1-oai-DiVA.org-bth-33262018-01-12T05:14:12ZAutomated Camera Placement using Hybrid Particle Swarm OptimizationengAutomated Camera Placement using Hybrid Particle Swarm OptimizationAmiri, Mohammad Reza ShamsRohani, SarmadBlekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknikBlekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik20143D ModellingParticle Swarm Optimization (PSO)Hybrid PSO (HPSO)Automatic Camera Placement (ACP)Region of Interest (RoI)Visibility DetectionComputer SciencesDatavetenskap (datalogi)Context. Automatic placement of surveillance cameras' 3D models in an arbitrary floor plan containing obstacles is a challenging task. The problem becomes more complex when different types of region of interest (RoI) and minimum resolution are considered. An automatic camera placement decision support system (ACP-DSS) integrated into a 3D CAD environment could assist the surveillance system designers with the process of finding good camera settings considering multiple constraints. Objectives. In this study we designed and implemented two subsystems: a camera toolset in SketchUp (CTSS) and a decision support system using an enhanced Particle Swarm Optimization (PSO) algorithm (HPSO-DSS). The objective for the proposed algorithm was to have a good computational performance in order to quickly generate a solution for the automatic camera placement (ACP) problem. The new algorithm benefited from different aspects of other heuristics such as hill-climbing and greedy algorithms as well as a number of new enhancements. Methods. Both CTSS and ACP-DSS were designed and constructed using the information technology (IT) research framework. A state-of-the-art evolutionary optimization method, Hybrid PSO (HPSO), implemented to solve the ACP problem, was the core of our decision support system. Results. The CTSS is evaluated by some of its potential users after employing it and later answering a conducted survey. The evaluation of CTSS confirmed an outstanding satisfactory level of the respondents. Various aspects of the HPSO algorithm were compared to two other algorithms (PSO and Genetic Algorithm), all implemented to solve our ACP problem. Conclusions. The HPSO algorithm provided an efficient mechanism to solve the ACP problem in a timely manner. The integration of ACP-DSS into CTSS might aid the surveillance designers to adequately and more easily plan and validate the design of their security systems. The quality of CTSS as well as the solutions offered by ACP-DSS were confirmed by a number of field experts. Sarmad Rohani: 004670606805 Reza Shams: 0046704030897Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:bth-3326Local oai:bth.se:arkivex913EE6CE06695C6BC1257CBD002B4DDDapplication/pdfinfo:eu-repo/semantics/openAccess |
collection |
NDLTD |
language |
English |
format |
Others
|
sources |
NDLTD |
topic |
3D Modelling Particle Swarm Optimization (PSO) Hybrid PSO (HPSO) Automatic Camera Placement (ACP) Region of Interest (RoI) Visibility Detection Computer Sciences Datavetenskap (datalogi) |
spellingShingle |
3D Modelling Particle Swarm Optimization (PSO) Hybrid PSO (HPSO) Automatic Camera Placement (ACP) Region of Interest (RoI) Visibility Detection Computer Sciences Datavetenskap (datalogi) Amiri, Mohammad Reza Shams Rohani, Sarmad Automated Camera Placement using Hybrid Particle Swarm Optimization |
description |
Context. Automatic placement of surveillance cameras' 3D models in an arbitrary floor plan containing obstacles is a challenging task. The problem becomes more complex when different types of region of interest (RoI) and minimum resolution are considered. An automatic camera placement decision support system (ACP-DSS) integrated into a 3D CAD environment could assist the surveillance system designers with the process of finding good camera settings considering multiple constraints. Objectives. In this study we designed and implemented two subsystems: a camera toolset in SketchUp (CTSS) and a decision support system using an enhanced Particle Swarm Optimization (PSO) algorithm (HPSO-DSS). The objective for the proposed algorithm was to have a good computational performance in order to quickly generate a solution for the automatic camera placement (ACP) problem. The new algorithm benefited from different aspects of other heuristics such as hill-climbing and greedy algorithms as well as a number of new enhancements. Methods. Both CTSS and ACP-DSS were designed and constructed using the information technology (IT) research framework. A state-of-the-art evolutionary optimization method, Hybrid PSO (HPSO), implemented to solve the ACP problem, was the core of our decision support system. Results. The CTSS is evaluated by some of its potential users after employing it and later answering a conducted survey. The evaluation of CTSS confirmed an outstanding satisfactory level of the respondents. Various aspects of the HPSO algorithm were compared to two other algorithms (PSO and Genetic Algorithm), all implemented to solve our ACP problem. Conclusions. The HPSO algorithm provided an efficient mechanism to solve the ACP problem in a timely manner. The integration of ACP-DSS into CTSS might aid the surveillance designers to adequately and more easily plan and validate the design of their security systems. The quality of CTSS as well as the solutions offered by ACP-DSS were confirmed by a number of field experts. === Sarmad Rohani: 004670606805 Reza Shams: 0046704030897 |
author |
Amiri, Mohammad Reza Shams Rohani, Sarmad |
author_facet |
Amiri, Mohammad Reza Shams Rohani, Sarmad |
author_sort |
Amiri, Mohammad Reza Shams |
title |
Automated Camera Placement using Hybrid Particle Swarm Optimization |
title_short |
Automated Camera Placement using Hybrid Particle Swarm Optimization |
title_full |
Automated Camera Placement using Hybrid Particle Swarm Optimization |
title_fullStr |
Automated Camera Placement using Hybrid Particle Swarm Optimization |
title_full_unstemmed |
Automated Camera Placement using Hybrid Particle Swarm Optimization |
title_sort |
automated camera placement using hybrid particle swarm optimization |
publisher |
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik |
publishDate |
2014 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3326 |
work_keys_str_mv |
AT amirimohammadrezashams automatedcameraplacementusinghybridparticleswarmoptimization AT rohanisarmad automatedcameraplacementusinghybridparticleswarmoptimization |
_version_ |
1718606808363302912 |