AUTOMATIC DETECTION OF VEHICLES IN OUTDOOR PARKING LOTS FROM ZENITH PERSPECTIVE USING NEURAL NETWORKS

摘要

Nowadays there arc a variety of methods to assist parking users in finding free sites in parking lots. However, there is no automatic system that takes into account the size of the car looking for a space or whether the cars adjacent to the free spaces are correctly parked. This paper presents a new method for detecting and calculating the area of vehicles in images taken from a zenith plane using computer vision and machine learning techniques that will help to create a vehicle-oriented search algorithm dedicated to finding the optimal spaces for vehicles entering an outdoor parking lot based on its characteristics. Results with scaled-down and real vehicles show that this new method can detect the area of the vehicles in an image with an average accuracy of 97.98%.