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Person re-identification combining deep features and attribute detection
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Watson, Gregory A. and Bhalerao, Abhir (2019) Person re-identification combining deep features and attribute detection. Multimedia Tools and Applications . doi:10.1007/s11042-019-08499-9 (In Press)
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WRAP-person-re-identification-deep-detection-Bhalerao-2019.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 16 December 2020. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (5Mb) |
Official URL: https://doi.org/10.1007/s11042-019-08499-9
Abstract
Attributes-Based Re-Identification is a way of identifying individuals when presented with multiple pictures taken under varying conditions. The method typically builds a classifier to detect the presence of certain appearance characteristics in an image, and creates feature descriptors based on the output of the classifier. We improve attribute detection through spatial segregation of a person’s limbs using a skeleton prediction method. After a skeleton has been predicted, it is used to crop the image into three parts - top, middle and bottom. We then pass these images to an attribute prediction network to generate robust feature descriptors. We evaluate the performance of our method on the VIPeR, PRID2011 and i-LIDS data sets, comparing our results against the state-of-the-art to demonstrate competitive overall matching performance.
| Item Type: | Journal Article | ||||||
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| Divisions: | Faculty of Science > Computer Science | ||||||
| Journal or Publication Title: | Multimedia Tools and Applications | ||||||
| Publisher: | Springer | ||||||
| ISSN: | 1380-7501 | ||||||
| Official Date: | 16 December 2019 | ||||||
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| Date of first compliant deposit: | 4 December 2019 | ||||||
| DOI: | 10.1007/s11042-019-08499-9 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Publisher Statement: | This is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11042-019-08499-9 | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
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