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dc.contributor.supervisorWang, Yang (Computer Science)en_US
dc.contributor.authorRahman, Tanzila
dc.date.accessioned2018-09-13T17:38:38Z
dc.date.available2018-09-13T17:38:38Z
dc.date.issued2018-06-15en_US
dc.date.submitted2018-08-12T19:44:10Zen
dc.identifier.urihttp://hdl.handle.net/1993/33350
dc.description.abstractPerson re-identification is a challenging task of matching a query person across multiple person's images or videos captured from different camera views. Recently, deep learning based approaches have showed promising performance on this task. In this thesis, initially we propose an image based person re-identification approach with Spatial Transformer Networks. Most previous deep learning based approaches use whole image features to compute the similarity between images. This is not very intuitive since not all the regions in an image contain information about the person identity. Hence, we introduce an end-to-end Siamese convolutional neural network that firstly localizes discriminative salient image regions and then computes the similarity based on these image regions. Furthermore, we propose an efficient attention based model for person re-identifying from videos. Our method generates an attention score for each frame based on frame-level features. The attention scores of all frames in a video are used to produce a weighted feature vector for the input video which is refined iteratively for re-identifying persons from videos. Extensive experiments on different datasets show that the proposed models provide an effective way of re-identifying person from images as well as videos.en_US
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectre-identification, Spatial transformer network, attention network, discriminative regionen_US
dc.titlePerson Re-identification in Images and Videosen_US
dc.typemaster thesisen_US
dc.degree.disciplineComputer Scienceen_US
dc.contributor.examiningcommitteeCarson Kai-Sang Leung (Computer Science), Jun Cai (Electrical and Computer Engineering)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteOctober 2018en_US


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