2021 Volume 2 Issue J2 Pages 821-832
Recently, research has been conducted on estimating posture and behavior using deep learning for the segmentation of object parts. For example, the segmentation of automobile parts can be used to detect a wrong-way driver or remodel an automobile. In our research, we attempt to apply deep learning to traffic censuses. During censuses, techniques to count automobiles by category from videos have been developed to save labor and improve the efficiency of work. However, these existing techniques have the problem of failing to classify automobiles with similar shapes. As a countermeasure to this problem, automobile type can be classified based on the shape of automobile parts focused on observations by surveyors. Therefore, in this research, we develop new techniques for the segmentation of automobile parts using deep learning. Furthermore, we discuss techniques for reducing the cost of re-learning by using automatically the generated training data. In the result, we obtained knowledge about the usefulness of these techniques through a demonstration experiment.
