Robust parking occupancy monitoring system using random forests

Abstract

In recent years, with the growing number of vehicles, the efficient parking management system has become necessary for large buildings. This paper presents a parking occupancy monitoring system which can automatically decide whether a vehicle is parked or empty in each parking space. There are many obstacles in the parking area, such as high diversity in car models, occlusion by other car, moving person, waste trash, and camera lens distortion, making it difficult to detect a vehicle. In order to solve all these problems, this paper proposes to use a part-based and machine learning-based vehicle detection algorithm. We demonstrate that the proposed method performs well on large indoor parking lot dataset which contains the above-mentioned obstacles.