USING LOW-COST LiDAR DYNAMIC POINT CLOUD DATA VERIFICATION OF APPLICABILITY TO PEDESTRIAN TRAFFIC VOLUME SURVEY

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  • 廉価なLiDARの動的な点群データを用いた歩行者交通量調査への適用可能性の検証

Abstract

<p> In current pedestrian traffic volume surveys, pedestrians passing through the surveyed cross section are generally counted manually, which limits the survey days and times. In recent years, there has been an increase in the number of surveys using video images taken by video cameras, but personal information and privacy must be taken into consideration. Therefore, LiDAR, which can measure the target pedestrian as a set of three-dimensional coordinate points, has been attracting attention. However, repetitive LiDAR cannot measure the measurement range exhaustively and is difficult to be applied to the survey. In this study, we conducted a pedestrian traffic survey using deep learning with point cloud data measured by non-iterative LiDAR, which can measure the measurement range exhaustively. The results of pedestrian counting showed that the correct response rate was 67.2% in the low case and 84.7% in the high case, indicating that the point cloud data measured by non-repeatable LiDAR has potential to be applied to pedestrian traffic volume surveys.</p>

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