Existing traffic infrastructure management to handle traffic congestion in major cities is aging and is not effective for traffic monitoring. With an increasing demand for developing cities as smart cities, advanced Intelligent transportation systems (ITS) are in need to improve the safety and traffic movements in the cities. As an application of ITS, vehicle re-identification has gained a wide interest in the field of robotics and computer vision. Currently, these tasks are performed from the data acquired by either of the standalone surveillance systems such as CCTV or UAV. Such data acquired for re-identification poses several challenges namely viewpoints, scale, illumination change, occlusion, etc. To address these, a hybrid surveillance system approach is proposed whereby an algorithm is developed for vehicle re-identification. The re-identification algorithm is tested on a dataset containing 33 identical vehicles observed across 20 CCTV cameras and a UAV. Re-identification of vehicles is performed by estimating a transformation that maps a vehicle observed in one modality to another. The performance of vehicle re-identification is compared for a CNN network trained with the vehicle identities with and without the application of homography.