A scalable system that enables law enforcement to identify traffic infractions in urban areas, issue fines efficiently, and
identify fraudulent license plate numbers.
Main Research question:
Sri Lanka's transportation sector faces a serious problem with road accidents, which impedes the nation's sustainable growth and
causes huge economic losses. To solve this issue, it is essential to implement modern road and traffic management technology to
minimize fatal accidents and traffic infractions, the leading causes of road accidents.
Individual research questions:
IT18223118: Vehicle tracking faces challenges in adapting to the environment, including low illumination, camera shudder, noise, less diverse data, and inefficient night detection. Daylight tracking is hindered by shadows, while limited and less diverse data hampers current research. Inadequate illumination further hampers night vision for efficient detection.
IT20501402: Automated license plate recognition (ANPR) systems are commonly used in various areas, such as parking enforcement, traffic management, and toll collection. However, their performance is affected by the quality of images. In addition to weather and lighting
conditions, other factors such as motion blur and occlusion can also affect the image quality of license plates.
IT20241728:
IT19029696:
Individual Objectives:
IT18223118: Track vehicles and detect lane line violation in adverse weather conditions and light conditions
IT20501402: Image quality Enhancement to detect number plates accurately and track vehicles
IT20241728: Red light passing violation detection and smart traffic system
IT19029696: Use computer vision to detect high speed traffic violations and analyze data to predict road accidents.
