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Vehicle Re-Identification Using Self-Supervised Vision Transformers


   Department of Computer Science

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Vehicle re-identification (Re-ID) is one of the primary components of an automated visual surveillance system. It aims to automatically identify/search vehicles in a multi-camera network usually having non- overlapping field-of-views. Majority of the approaches dealing with the re-ID problem tackle it in a supervised manner which have certain limitations that pose challenges of generalization e.g., large amount of annotated data is required for training and is often limited to the dynamic growth of the data. Unsupervised learning techniques can potentially cope with such issues by drawing inference directly from the unlabelled input data and have been effectively employed in the context of person re-ID. To this end, the work in this thesis is intended to formulate the whole vehicle re-ID problem into an self-supervised Siamese-like learning paradigm using vision transformers.

First degree in Computer Science with 2:1 or above MSc degree in the relevant subject areas is desired


Funding Notes

There is no funding associated with the PhD study. However applicants are encouraged to apply for funding from any funding bodies.

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