Abstract
Due to the lack of wide availability of parking assisting applications, vehicles tend to cruise more than necessary to find an empty parking space. This problem is evident globally and the intensity of the problem varies based on the demand of parking spaces. It is a well-known hypothesis that the amount of cruising by a vehicle is dependent on the availability of parking spaces. However, the amount of cruising that takes place in search of parking spaces within a parking lot is not researched. This lack of research can be due to privacy and illumination concerns with suitable sensors like visual cameras. The use of thermal cameras offers an alternative to avoid privacy and illumination problems. Therefore, this paper aims to develop and demonstrate a methodology to detect and track the cruising patterns of multiple moving vehicles in an open parking lot. The vehicle is detected using Yolov3, modified Yolo, and custom Yolo deep learning architectures. The detected vehicles are tracked using Kalman filter and the trajectory of multiple vehicles is calculated on an image. The accuracy of modified Yolo achieved a positive detection rate of 91% while custom Yolo and Yolov3 achieved 83% and 75%, respectively. The performance of Kalman filter is dependent on the efficiency of the detector and the utilized Kalman filter facilitates maintaining data association during moving, stationary, and missed detection. Therefore, the use of deep learning algorithms and Kalman filter facilitates detecting and tracking multiple vehicles in an open parking lot.
Highlights
Congestion and pollution from traffic are major problems in many urban areas
Concluding Discussion e usage of deep learning algorithms and Kalman filter performed with good accuracy in detection and tracking of the movement of multiple vehicles in an open parking lot with varying environmental and illumination conditions
A combination of thermal camera and deep learning algorithms enabled the detection of objects in varying illumination and environmental conditions. e mod Yolo algorithm performed better compared to Yolov3 and customized Yolo
Summary
Congestion and pollution from traffic are major problems in many urban areas. E majority of the high traffic flows are related to the low supply of parking spaces. Is indicates that congestion and excess driving that occur during the search of vacant parking spaces lead to increased pollution from the traffic. To understand the magnitude of this problem, the cruising of vehicles should be captured to comprehend how people drive in a parking lot. According to [4], the time taken to occupy an empty parking space is approximately 1.18 minutes and the data were collected using GPS. In another study [5], a visual camera was placed on traffic signal poles to capture the number of vehicles cruising for parking in an on-street parking lot. There are limited empirical cruising data available within parking lots
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