| dc.description.abstract | The vehicle is an extensively studied object due to its various intriguing features that require enhancement. Important aspects of a vehicle, such as defining its direction, calculating distance, and determining its speed, play a vital role in its overall functionality. Although these features can be directly derived from monocular videos, they often face challenges related to calibration and occlusion. Vehicle speed estimation poses a notable challenge within the realm of intelligent transportation systems (ITS) research. Although conventional and deep learning methods have exhibited potential in this domain, their progress has been hindered by the substantial expenses associated with implementing hardware devices for data gathering via sensors like lidar, radar, and magnetic sensors. In this dissertation, we propose a model that consists of two main components. The first component is a vehicle detection and tracking module, designed to accurately detect and track specific objects while addressing calibration issues. The second component is a homography transformation regression network, which efficiently and accurately estimates vehicle speed while addressing occlusion issues in monocular videos. Through experimental evaluations on multiple datasets, we demonstrate that our proposed method outperforms state-of-the-art approaches, achieving a significant improvement of approximately 51.00% in the mean square error (MSE) metric. | en_US |