An automatic skateboarder detection method with roadside LiDAR data

The increasing conflicts between skateboarders and pedestrians on the campuses have caused safety concerns. Although traffic planners on campus usually did not consider skateboarding into planning due to the lack of historical skateboarding data. The traditional data collection method such as manual counting or video detection took a lot of effort and could only provide macrolevel skateboarding data. Therefore, this research presented a new approach for skateboarder detection using the roadside LiDAR sensor. A two-stage classification method was developed to distinguish skateboarders and other road users. The first stage was to classify motor vehicles and nonvehicles (pedestrians, bicycles, and skateboarders). Then the second step was to distinguish skateboarders from pedestrians and bicycles. The proposed procedure was evaluated using real-world data on campus. The results showed that the proposed procedure can detect the skateboarder with the overall accuracy of 89.5%. The data collected in the real world showed that the speed of the skateboarder was usually higher than the pedestrian and lower than the bicycle. The skateboarding information extracted from the proposed detection method could be applied for skateboarder behavior analysis, volume counting, and safety analysis. A level-based procedure to reinforce the safety between skateboarders and pedestrians was recommended.

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    • © 2019 Taylor & Francis Group, LLC and The University of Tennessee. Abstract reprinted with permission of Taylor & Francis.
  • Authors:
    • Wu, Jianqing
    • Xu, Hao
    • Yue, Rui
    • Tian, Zong
    • Tian, Yuan
    • Tian, Yuxin
  • Publication Date: 2021-3


  • English

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  • Accession Number: 01768360
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 9 2021 7:43AM