Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.contributor.advisor | Lun, P. K. Daniel (EIE) | en_US |
| dc.creator | Li, Jiaying | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/11191 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Person re-identification with deep learning | en_US |
| dcterms.abstract | The Aligned Reid neural network is a complex network for person re-identification. In the training stage, it needs to consider the global and local features of multiple images at the same time. The processing of local features is very innovative in the Reid field. To implement this process, we need to cut the image horizontally and compute the local features of each part. Based on this step, we propose a new method to improve the performance. When computing the distance between two images, it is necessary to find the shortest distance between the two images by one-to-one correspondence of the cut local images according to the characteristics. Based on the original Aligned Reid method, this dissertation further optimizes the evaluation of the loss function. We use the center loss method, which focuses on the inter-class distance in image classification, to improve the SoftMax loss result used to obtain the global features. At the same time, this method makes up for the disadvantage of the triple loss method which only considers the relative distance, and improves the network performance to a certain extent. | en_US |
| dcterms.extent | vi, 29 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2021 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Pattern recognition systems | en_US |
| dcterms.LCSH | Biometric identification | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5667.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 703.15 kB | Adobe PDF | View/Open |
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