Authors: Jamila J, M. Sathya, R. Priyadharshini, J. Jenshya, V. Vinothini
Abstract: Urban areas face growing challenges in managing parking efficiently due to increased vehicle density. This paper proposes a real-time parking occupancy detection system using OpenCV, a powerful open- source computer vision library. By analyzing live video feeds from strategically positioned cameras, the system detects, classifies, and tracks vehicles using advanced object detection techniques, such as YOLO and SSD. This enables continuous monitoring of parking spaces and accurate assessment of their occupancy status. The system operates in three core stages: vehicle detection, classification of parked or moving vehicles, and real-time tracking using algorithms like Kalman filters or optical flow. Occupancy data is dynamically updated and shared via user-friendly interfaces such as mobile apps or digital displays, helping drivers find available spots efficiently. The system is developed using Python and OpenCV to ensure flexibility and ease of deployment across different parking environments. Performance evaluation was carried out using real-world datasets under various lighting and environmental conditions, demonstrating high accuracy and responsiveness. The proposed solution is scalable, adaptable to various camera setups, and suitable for deployment in street parking, garages, and smart city infrastructure. By improving parking space utilization and reducing the time spent searching for parking, this system contributes to easing traffic congestion, reducing fuel consumption, and enhancing the urban driving experience. With potential features such as safety compliance monitoring and modular architecture, the proposed system represents a significant step toward intelligent and efficient parking management in modern cities.
