Transactions of the Japan Society for Industrial and Applied Mathematics
Online ISSN : 2424-0982
ISSN-L : 0917-2246
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Volume 35, Issue 1
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Application
  • Pedestrian Identifying and Tracking Algorithm for Point Cloud Data with Sparse Problem
    Sora Kurihara, Akiyasu Tomoeda
    2025 Volume 35 Issue 1 Pages 1-18
    Published: 2025
    Released on J-STAGE: March 31, 2025
    DOIhttps://doi.org/10.11540/jsiamt.35.1_1
    JOURNAL FREE ACCESS
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    Abstract. In this study, we propose a method for identifying and tracking pedestrians from 3D point cloud data by combining the CVC method and an improved tracking algorithm. For pedestrian identification, the previous CVC method is used, and for tracking, the tracking algorithm improved in this study is used to solve the density problem of point clouds and the separation problem of pedestrians in close proximity. We confirmed that the proposed method can detect the actual number of pedestrians more accurately than existing methods, and can also sufficiently solve the problem of occlusion.

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