RESEARCH AND DEVELOPED FOR SEGMENTATION OF AUTOMOBILE PARTS ON TRAFFIC CENSUS

DOI 2 Citations

Bibliographic Information

Other Title
  • 交通量調査のための車両部位識別技術に関する研究

Description

<p>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.</p>

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Details 詳細情報について

  • CRID
    1390853038535238272
  • NII Article ID
    130008118398
  • DOI
    10.11532/jsceiii.2.j2_821
  • ISSN
    24359262
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
    • Crossref
  • Abstract License Flag
    Disallowed

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