Abstract:
Vehicle re-identification has become a key area in computer vision which has im portant applications in areas like vehicle tracking, commodity tracking, security
tracking, etc. But Vehicle re-identification inherits two challenges; there are large
variations between the similar classes, similar classes have different looks in dif ferent viewpoints, and slight differences between different classes, two different
vehicles may look the same as companies are making vehicles with similar ap pearances. Various CNN-based Vehicle Re-ID have been proposed but CNN faces
two main challenges; only contain the information of neighborhood and loss of
information due to sampling and convolutions. Transformer have recently been
introduced in vision community and achieving superior results because they can
solve fundamental problems of CNNs by keep long term relation and not losing
any information. A novel approach based on Shifted Window Transformer was
introduced that in this thesis that uses transformers model to tackle the vehicle
re-id problem and uses side information module to enhance the discrimination
ability of the model. To the best of our knowledge Shifted Window Transformer
have first-time been used as a backbone in vehicle re-identification task and results
were promising, the model was evaluated on benchmark dataset VeRi.
