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dc.contributor.advisorYan, Wei Qi
dc.contributor.advisorLiu, William
dc.contributor.authorShen, Jun
dc.date.accessioned2017-07-02T21:33:20Z
dc.date.available2017-07-02T21:33:20Z
dc.date.copyright2016
dc.date.created2017
dc.identifier.urihttp://hdl.handle.net/10292/10606
dc.description.abstractComputer vision based surveillance systems have become increasingly important to society. This thesis presents a new approach for computer vision based alarm making systems which detect abnormal events in fixed camera circumstances. The approach contains four functions: namely (1) detecting, (2) tracking, (3) recognizing and (4) alarming. In line with these functions, based on the results of detecting, tracking and recognition, the system will be able to generate alarms automatically. Through the experiments, the related methods and algorithms applied to the proposed approach provide better performance for the purpose of alarm making, thus it could be helpful in reducing the manual labor of security staff. The contributions of this thesis are: Firstly, the shortcomings and deficiencies of the traditional surveillance and alarm systems have been studied. Secondly, computer vision techniques have been utilized to allow the system to work with different environments. Thirdly, dual artificial neural networks have been innovatively deployed for abnormal events detection and to improve the accuracy of alarming to reduce false alarms. The overall result for the false alarm rate of the system developed in this project is 13.8% which is lower that the mainstream 15.27% and also helpful for the management of traffic environments. In future, the improvement of the system will be the working direction for the researcher such as using more training datasets to make the abnormal events alarming system more efficient in terms of abnormal event detection and reduction of the false alarm rate.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectIntelligence surveillanceen_NZ
dc.subjectAlarmen_NZ
dc.subjectTrafficen_NZ
dc.subjectKalman filteren_NZ
dc.subjectGMMen_NZ
dc.subjectANNen_NZ
dc.subjectHOGen_NZ
dc.subjectLBPen_NZ
dc.subjectINRIAen_NZ
dc.titleSurveillance Alarm Makingen_NZ
dc.typeThesis
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2017-06-30T04:00:35Z


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