Vehicle Occurrence-based Parking Space Detection
Paulo R. Lisboa de Almeida Jeovane Honório Alves Luiz S. Oliveira Andre Gustavo Hochuli João V. Fröhlich Rodrigo A. Krauel
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\%.