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dc.contributor.advisorAnwar, Md. Tawhid
dc.contributor.advisorAhmed, Md. Sabbir
dc.contributor.authorAmin, Nafiun Al
dc.contributor.authorHaider, Ayan
dc.contributor.authorNaomi, Tasmia Tarannum
dc.contributor.authorRahman, Rafid Sadman
dc.contributor.authorZaman, Nusaiba
dc.date.accessioned2024-12-03T10:18:57Z
dc.date.available2024-12-03T10:18:57Z
dc.date.copyright©2024
dc.date.issued2024-07
dc.identifier.otherID 20201069
dc.identifier.otherID 20201152
dc.identifier.otherID 20241013
dc.identifier.otherID 23141033
dc.identifier.otherID 20201104
dc.identifier.urihttp://hdl.handle.net/10361/24865
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.en_US
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 39-43).
dc.description.abstractThe use of security surveillance systems is becoming more prevalent, causing them to become an excellent tool for major crime prevention. However, niche crimes such as the use of cigarettes and other forms of smoking devices in non-smoking environments go unnoticed due to the range of motions required to perform smoking being very minimal. The problem is not only smoking but also the littering that follows afterward. Hence, our research implemented several models that can recognize smoking-related aspects. With these models, it would be possible to detect smokers through surveillance systems and prevent such niche crimes without requiring extensive manpower. In this research, we have used customized visual data to train our models, consisting of videos of smoking from different angles, lighting conditions, and population densities. Object detection models such as YOLOv5, YOLOv8, YOLOv9 and YOLOv10 were used to detect objects related to smoking and classify people as smokers, with YOLOv9 achieving the best results at 96.2% Precision and 76.3% Recall scores. Video Classification was also performed using YOLOv8 to recognize the different features in frames that constitutes to a person being a smoker which achieved a Precision of 90.6% and Recall of 94.2%.en_US
dc.description.statementofresponsibilityNafiun Al Amin
dc.description.statementofresponsibilityAyan Haider
dc.description.statementofresponsibilityTasmia Tarannum Naomi
dc.description.statementofresponsibilityRafid Sadman Rahman
dc.description.statementofresponsibilityNusaiba Zaman
dc.format.extent52 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectSecurity surveillance systemen_US
dc.subjectCigarettesen_US
dc.subjectSmokingen_US
dc.subjectHuman activitiesen_US
dc.subjectVideo Classificationen_US
dc.subject.lcshSmoking--Social aspects.
dc.subject.lcshCriminal behavior--Prevention.
dc.subject.lcshHuman activity recognition (Computer vision).
dc.titleEfficient monitoring of illicit activities: identifying smokers through human activity recognitionen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB.Sc. in Computer Science 


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