An AI-Enabled Vehicle Surveillance System to Tracking Entrance, Exit, and Parking of Vehicles on the University of Technology–Jamaica, Papine Campus

There is an increased number of vehicles on university campuses such as the University of Technology–Jamaica. This necessitates better tracking of vehicles entering and leaving the campus and identifying parking violations. One of the more prominent ways of addressing these issues in other organizations is the implementation of automated parking systems. These implementations generally use sensor technology for vehicle detection along with artificial intelligence in the form of automatic license plate readers to recognize and record vehicle license plates. Automated parking systems, however, generally lack features that are specifically designed to aid in security and are not often used by institutions and businesses to aid in management. We designed a system, which we named Campus Vision, to tackle misplacement, tracking, and parking of vehicles on the University of Technology, Jamaica, Papine Campus. This chapter presents our methodology, results, conclusions, and recommendations. We designed our system using the python programming language along with the YOLO and EasyOCR libraries. The system analyzed, extracted, and stored images from the live stream of video from select security cameras at the university. Our results showed that Campus Vision successfully identified 78% of vehicles entering and 71% of vehicles exiting the campus, and of these vehicles identified, it recognized 98% of the license plates. Campus Vision successfully detected 90% of submitted vehicles involved in parking violations, with 100% license plate detection. We concluded that Campus Vision was able to achieve an overall accuracy rate of 76% in detecting vehicles and 98% in detecting license plates while only being able to achieve a 34% accuracy rate in correctly identifying license plate numbers. We recommend that the system be considered for implementation but with dedicated GPUs and better strategic placement of security cameras. We further recommend that more research be conducted in this area, for example, with motorcycles, trucks, and buses on campus.