Comparing image features and machine learning algorithms for real - time parking space classification

Finding a vacant parking lot in urban areas is mostly time-consuming and not satisfying for potential visitors or customers. Efficient car-park routing systems could support drivers to find an unoccupied parking lot. Current systems detecting vacant parking lots are either very expensive due to the hardware requirement or do not provide a detailed occupancy map. In this paper, we propose a video-based system for low-cost parking space classification. A wide-angle lens camera is used in combination with a desktop computer. We evaluate image features and machine learning algorithms to determine the occupancy of parking lots. Each combination of feature set and classifier was trained and tested on our dataset containing approximately 10,000 samples. We assessed the performance of all combinations of feature extraction and classification methods. Our final system, incorporating temporal filtering, reached an accuracy of 99.8 %.