This thesis addresses key challenges in intersection traffic monitoring using overhead fisheye cameras, focusing on object detection, localization, vehicle maneuver classification, and traffic violation detection. A data augmentation technique was developed to improve the performance of deep learning-based object detection algorithms for fisheye images. By fine-tuning these models, significant improvements in Average Precision (AP) were achieved for vehicle and pedestrian detection, effectively addressing object orientation and size variability. A novel calibration method was introduced to mitigate the effects of road surface elevation changes on object localization. This method accurately translates image coordinates into geographical coordinates by incorporating 3D road characteristics. The proposed localization algorithm, validated through field tests, demonstrated high accuracy in localizing both cars and pedestrians. Furthermore, Kalman filtering techniques were integrated to enhance object tracking, providing precise localization even in complex environments like sloped streets. In addition, a self-learning vehicle maneuver classification and counting algorithm was developed, capable of recognizing various vehicle movements such as turns and U-turns. The algorithm’s performance was validated in real-world scenarios, where it successfully classified and counted vehicle maneuvers at multiple intersections. Moreover, a traffic violation detection system was designed on top of the maneuver classification algorithm to identify common infractions like box-blocking and illegal turns at intersections. The outcomes of this research contribute to a comprehensive system that enhances traffic monitoring, safety enforcement, and operational efficiency at intersections, offering practical solutions to modern traffic management challenges. / Thesis / Doctor of Philosophy (PhD) / This thesis explores methods for improving intersection traffic monitoring using overhead fisheye cameras. It enhances vehicle and pedestrian detection with a refined object detection model that improves accuracy by effectively handling object orientation and size variations. A new calibration technique has been developed to address road surface elevation changes, improving the precision of object localization. The thesis also presents a novel object localization approach that combines Kalman filtering with camera altitude correction, enabling accurate object localization in complex environments like sloped streets. Additionally, a new vehicle counting algorithm is designed to handle fisheye imagery and traffic management challenges. This system has proven effective in real-world tests, accurately classifying the vehicle maneuvers used to detect traffic violations such as illegal turns and box-blocking with an impressive precision rate. The proposed methods significantly enhance real-time traffic monitoring and enforcement, contributing to safer and more efficient intersections.