dc.contributor.authorTang, Jinli
dc.description.abstractPerson Re-Identification is a new technique compared to the person identification, which is more difficult and complex. It is easily affected by the surrounding environment and the person's gestures. Although Person Re-ID has made great progress in recent years, there are still a lot of problems that need to be solved. In this thesis, the basic knowledge about person identification is introduced and reviewed. It then we introduced the image based Person Re-ID and video based Person Re-ID. For the theory and method, the TDL distance learning model was studied. Besides the TDL based method, the methods involving feature extration are also introduced. For the color feature, we used the localised average colour histogram. For the space-time feature, the HOG3d descriptor was used. Two datasets including iLIDS and PRID2011 were used for the experiment. Both experiments obtained good results: the Rank 1 rate for iLIDS is about 57% and the Rank 1 rate for PRID2011 is about 55%. They are better than the previous results, which lays the foundation for future work.en_US
dc.format.extent57 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titlePerson re-identification for video surveillanceen_US
dc.contributor.supervisorChau Lap Puien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Communications Engineering)en_US

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