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.