dc.contributor.authorYuan, Ziying
dc.date.accessioned2015-06-09T04:17:01Z
dc.date.available2015-06-09T04:17:01Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/64903
dc.description.abstractWide use of video surveillance systems calls for powerful tools to extract information from video data. In this dissertation, object detection and tracking algorithms are the focus of study. Three different object detection and two different tracking algorithms, which have gained their popularity in computer vision research community, are investigated in this dissertation. The three object detection algorithms investigated in this dissertation are background subtraction with adaptive Gaussian mixture model, Histogram of Oriented Gradients (HOG) detector and Deformable Part Model (DPM) detector. Background subtraction with Gaussian mixture model can detect moving objects fast and accurately in static environment. HOG detector and DPM detector can discriminate objects over background if trained before. For object tracking, the classical algorithm, Kalman filter, is studied. The Tracking-Learning-Detector (TLD) algorithm is also studied, which is a powerful tool for long-term detection and tracking. In this dissertation, the above algorithms are evaluated on two standard benchmark datasets, i.e. TUD-Stadmitte and PETS-2009 S2/Ll. The performance of these algorithms are reported and discussed.en_US
dc.format.extent52 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.titleObject detection and tracking from surveillance videosen_US
dc.typeThesis
dc.contributor.supervisorKap Luk Chanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMSC(SIGNAL PROCESSING)en_US


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