  Parking guidance information (PGI) systems are used to provide information to
drivers about the nearest parking lots and the number of vacant parking slots.
Recently, vision-based solutions started to appear as a cost-effective
alternative to standard PGI systems based on hardware sensors mounted on each
parking slot. Vision-based systems provide information about parking occupancy
based on images taken by a camera that is recording a parking lot. However,
such systems are challenging to develop due to various possible viewpoints,
weather conditions, and object occlusions. Most notably, they require manual
labeling of parking slot locations in the input image which is sensitive to
camera angle change, replacement, or maintenance. In this paper, the algorithm
that performs Automatic Parking Slot Detection and Occupancy Classification
(APSD-OC) solely on input images is proposed. Automatic parking slot detection
is based on vehicle detections in a series of parking lot images upon which
clustering is applied in bird's eye view to detect parking slots. Once the
parking slots positions are determined in the input image, each detected
parking slot is classified as occupied or vacant using a specifically trained
ResNet34 deep classifier. The proposed approach is extensively evaluated on
well-known publicly available datasets (PKLot and CNRPark+EXT), showing high
efficiency in parking slot detection and robustness to the presence of illegal
parking or passing vehicles. Trained classifier achieves high accuracy in
parking slot occupancy classification.
