Parking finder mobile application - Tham, Chee Ming (2025)
With the rise in the number of car owners in fast-growing metropolitan areas, the need for effective parking solutions is becoming more demanding. This project proposes a Parking Finder Mobile Application that will provide real-time information about parking space availability and the parking finding status of vehicles in the parking lot. In this system, computer vision and deep learning models such as YOLOv8 will be utilized for parking spaces and vehicle detection while the DeepSORT algorithm is implemented to track vehicle movement in real-time. The proposed solution tackles the limitations that existing parking systems have including the high cost of implementation and lack of real-time vehicle monitoring. Combining parking space detection with vehicle tracking, the program will shorten the parking search times and improve user experience using a colour-coded status indicator and a simple interface. It is anticipated that such an approach would optimize parking space utilization in cities and promote urban mobility.