Automated monitoring of pedestrians on non-motorized facilities with high pedestrian flows is challenging. Several automated sensor solutions are commercially available that have been evaluated in the literature including traditional point-based sensors, such as inductive loop detectors for bicycles and infrared sensors for pedestrians. More recently, image-based systems, based on video cameras or thermal video cameras, have been developed. Despite the various options, some key limitations of existing solutions exist, in particular, the lack of low-cost solutions using embedded systems capable of performing in real-time under high volume (flow) conditions. This work aims at developing and evaluating the performance of a novel, real-time counting system, developed for environments with high pedestrian flows. The proposed system is based on emerging LiDAR (Light Detection and Ranging) technology. As an input, the system uses the distance measurements from a two-dimensional LiDAR sensor with a set of distinct laser channels and a given angular resolution between each channel. The developed system processes those measurements using a clustering algorithm to detect, count, and identify the direction of travel of each pedestrian. The system’s performance is evaluated by comparing its directional counting outputs with manual counts (ground truth) using disaggregate and aggregate (15-minutes interval) counts at two different monitoring locations. The results demonstrate that the system accurately counts more than 97% of the pedestrians at the disaggregate level, with a false direction detection rate of 1.1%. The over-counting error is 0.7% and the under-counting errors are 1.3% and 2.7% for the two selected sites. At the aggregate level (15-minutes interval), the average absolute percentage deviations (AAPDs) are 1.6% and 4.3% while the weighted AAPDs are 1.5% and 3.5% for the first and second sites, respectively. The accuracy of the proposed system is higher than the traditional technologies used for the same purpose.

在行人流量大的非机动设施上对行人进行自动监视具有挑战性。几种自动化传感器解决方案在市场上都有售，并且已经在文献中进行了评估，包括传统的基于点的传感器，例如用于自行车的感应环路检测器和用于行人的红外传感器。最近，已经开发了基于摄像机或热摄像机的基于图像的系统。尽管有各种选择，但现有解决方案仍存在一些关键限制，特别是缺乏使用能够在大容量（流量）条件下实时执行的嵌入式系统的低成本解决方案。这项工作旨在开发和评估针对行人流量大的环境开发的新型实时计数系统的性能。拟议的系统基于新兴的LiDAR（光检测和测距）技术。作为输入，系统使用来自二维LiDAR传感器的距离测量值，该传感器具有一组不同的激光通道，并且每个通道之间具有给定的角度分辨率。开发的系统使用聚类算法处理这些测量值，以检测，计数和识别每个行人的行进方向。通过在两个不同的监视位置使用分解计数和聚合计数（15分钟间隔）将定向计数输出与手动计数（真实情况）进行比较，从而评估系统的性能。结果表明，该系统在分类​​级别上准确地统计了97％以上的行人，错误方向检测率为1.1％。超算错误为0。7％，两个选定网站的计数不足错误分别为1.3％和2.7％。在总体水平（每隔15分钟），第一和第二个站点的平均绝对百分比偏差（AAPD）分别为1.6％和4.3％，而加权AAPD分别为1.5％和3.5％。提出的系统的准确性高于用于相同目的的传统技术。