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Automated Camera Placement using Hybrid Particle Swarm Optimization
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesisAlternative title
Automated Camera Placement using Hybrid Particle Swarm Optimization (Swedish)
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. , 125 p.
Keyword [en]
3D Modelling, Particle Swarm Optimization (PSO), Hybrid PSO (HPSO), Automatic Camera Placement (ACP), Region of Interest (RoI), Visibility Detection
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-3326Local ID: oai:bth.se:arkivex913EE6CE06695C6BC1257CBD002B4DDDOAI: oai:DiVA.org:bth-3326DiVA: diva2:830630
Uppsok
Technology
Supervisors
Note
Sarmad Rohani: 004670606805 Reza Shams: 0046704030897Available from: 2015-04-22 Created: 2014-04-17 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • modern-language-association-8th-edition
  • vancouver
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More styles
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  • Other locale
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