Vehicle Occurrence-based Parking Space Detection
This addresses the cost and feasibility issues in smart-parking solutions for urban management, though it is incremental as it builds on existing computer vision methods.
The paper tackled the problem of automatic parking space detection without manual labeling by using instance segmentation and vehicle occurrence to generate heat maps, achieving up to 95.60% AP25 and 79.90% AP50 scores on parking lot datasets.
Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. Computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60\% and AP50 score up to 79.90\%.