Car Parking Vacancy Detection and Its Application in 24-Hour Statistical Analysis

Finding empty parking spaces is a common problem in densely populated areas. Drivers spend an unnecessarily large amount of time searching for the empty spots, because they do not have perfect knowledge about the available vacant spots. An effective vacancy detection system would significantly reduce search time and increase the efficiency of utilizing the scarce parking spaces. The proposed solution uses trained neural networks to determine occupancy states based on visual features extracted from parking spots. This method addresses three technical problems. First, it responds to changing light intensity and non-uniformity by having adaptive reference pavement pixel value calculate the color distance between the parking spots in question and the pavement. Second, it approximates images with limited lighting to have similar feature values to images with sufficient illumination, merging the two patterns. Third, the solution separately considers nighttime vacancy detection, choosing appropriate regions to obtain reference color value. The accuracy was 99.9% for occupied spots and 97.9% for empty spots for this 24-hour video. Besides giving an accurate depiction of the car park's utilization rate, this study also reveals the patterns of parking events at different times of the day and insights to the activities that car drivers engage with.