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Sensors and Materials, Volume 33, Number 1(1) (2021)
Copyright(C) MYU K.K.
pp. 17-34
S&M2435 Research Paper of Special Issue
Published: January 15, 2021

A Method for Detecting Street Parking Using Dashboard Camera Videos [PDF]

Akihiro Matsuda, Tomokazu Matsui, Yuki Matsuda, Hirohiko Suwa, and Keiichi Yasumoto

(Received July 29, 2020; Accepted October 21, 2020)

Keywords: street parking, dashboard camera, city sensing, object detection, machine learning

In recent years, street parking in prohibited areas has become a social problem, especially in urban and tourist areas. In addition, because street parking can cause traffic congestion and accidents, real-time detection is required. The detection of street parking has been previously implemented on the basis of comparisons of videos recorded by fixed-point cameras. However, this approach has a limited detection area and low accuracy. To overcome these problems, this study aims towards a real-time street parking detection system that uses dashboard camera videos. We propose a machine learning method based on the characteristics of on-street parked vehicles derived by transforming images into text. The object detection model YOLOv3 was used to analyze videos. We created a dataset based on the coordinate information of 1765 vehicles and the recording vehicle information. We also created a model using random forest and logistic regression algorithms and evaluated it using the holdout and stratified 5-fold validation methods. F-measure values of up to 92% and 89% were obtained for the two types of model, respectively. These results confirm the effectiveness of the proposed street parking detection method based on bounding boxes and recording vehicle data.

Corresponding author: Akihiro Matsuda

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Cite this article
Akihiro Matsuda, Tomokazu Matsui, Yuki Matsuda, Hirohiko Suwa, and Keiichi Yasumoto, A Method for Detecting Street Parking Using Dashboard Camera Videos, Sens. Mater., Vol. 33, No. 1, 2021, p. 17-34.

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