  In recent years, the development of robust Intelligent transportation systems
(ITS) is tackled across the globe to provide better traffic efficiency by
reducing frequent traffic problems. As an application of ITS, vehicle
re-identification has gained ample interest in the domain of computer vision
and robotics. Convolutional neural network (CNN) based methods are developed to
perform vehicle re-identification to address key challenges such as occlusion,
illumination change, scale, etc. The advancement of transformers in computer
vision has opened an opportunity to explore the re-identification process
further to enhance performance. In this paper, a framework is developed to
perform the re-identification of vehicles across CCTV cameras. To perform
re-identification, the proposed framework fuses the vehicle representation
learned using a CNN and a transformer model. The framework is tested on a
dataset that contains 81 unique vehicle identities observed across 20 CCTV
cameras. From the experiments, the fused vehicle re-identification framework
yields an mAP of 61.73% which is significantly better when compared with the
standalone CNN or transformer model.
