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Video-based person re-Identi fication

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dc.contributor.author Kaur, Gunkirat
dc.contributor.author Vatsa, Mayank (Advisor)
dc.contributor.author Singh, Richa (Advisor)
dc.date.accessioned 2019-10-09T05:35:39Z
dc.date.available 2019-10-09T05:35:39Z
dc.date.issued 2019-04-15
dc.identifier.uri http://repository.iiitd.edu.in/xmlui/handle/123456789/766
dc.description.abstract Person Re-identi fication problem aims at matching the same person in non-overlapping cameras. Earlier, most of the person re-identi fication problems were focused on image-based solutions. But with the increase in surveillance and cameras, video-based solutions are used. The video-based solution gives a better result for person re identifi cation as it includes spatial and temporal information of the person which is not present in the single image. In this project, I worked on two models. First, two-stream convolutional networks (TSF-CNN) for extracting spatial and temporal features in videos. I evaluated this model on UCF101 Dataset. Second, I proposed a model using Spatial-Temporal Attention network, TSF-CNN and Attribute network for video-based person re-identifi cation. The TSF-CNN network learns the spatial and temporal features whereas the attribute network learns the attribute of the person. I evaluated this model on MARS and iLIDS-VID Dataset. en_US
dc.language.iso en_US en_US
dc.publisher IIITD-Delhi en_US
dc.subject Person Re-Identifi cation en_US
dc.subject Spatial en_US
dc.subject Temporal en_US
dc.subject Temporal Segment Network en_US
dc.subject Attribute en_US
dc.subject Attention en_US
dc.title Video-based person re-Identi fication en_US
dc.type Other en_US

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