The project focused on developing a real-time free parking spot detection system using computer vision technology. The goal was to improve parking lot efficiency by accurately identifying vacant parking spaces and displaying the results to drivers instantly, helping them save time and reduce congestion. The system uses object detection algorithms to analyze live video feeds and identify both parked cars and available spaces.
The Challenge
Lighting Conditions: The system had to work seamlessly under varying light conditions, such as bright sunlight, nighttime darkness, and shadows cast by vehicles. Real-Time Processing: The challenge of processing video feeds in real-time to ensure that parking availability data is up-to-date, minimizing delays.
Accuracy in High Traffic: Ensuring the system maintained high accuracy even in crowded parking lots where vehicles are constantly moving, avoiding false positives or missed detections.
The Solution
Computer Vision: Leveraged object detection algorithms to identify both parked cars and open parking spaces from video feeds.
Real-Time Processing: Used advanced deep learning frameworks like TensorFlow or PyTorch to analyze video feeds in real-time, ensuring immediate updates on parking availability.
Edge Computing: Deployed the solution on edge devices close to the parking area to reduce latency and ensure quick processing and response times.
The End Result
Deployment: The system was successfully deployed and is now operational in parking lots with 700 spots in production.
Impact: It provides real-time information on parking availability, optimizing parking lot usage, reducing traffic congestion, and improving the overall parking experience for drivers.
