The integration of Light Detection and Ranging (LiDAR) and Internet of Things
(IoT) technologies offers transformative opportunities for public health
informatics in urban safety and pedestrian well-being. This paper proposes a
novel framework utilizing these technologies for enhanced 3D object detection
and activity classification in urban traffic scenarios. By employing elevated
LiDAR, we obtain detailed 3D point cloud data, enabling precise pedestrian
activity monitoring. To overcome urban data scarcity, we create a specialized
dataset through simulated traffic environments in Blender, facilitating
targeted model training. Our approach employs a modified Point
Voxel-Region-based Convolutional Neural Network (PV-RCNN) for robust 3D
detection and PointNet for classifying pedestrian activities, significantly
benefiting urban traffic management and public health by offering insights into
pedestrian behavior and promoting safer urban environments. Our dual-model
approach not only enhances urban traffic management but also contributes
significantly to public health by providing insights into pedestrian behavior
and promoting safer urban environment.

利用光探测与测距（LiDAR）和物联网（IoT）技术的整合，在城市安全和行人福祉方面为公共卫生信息学提供了变革性机会。本文提出了一种利用这些技术进行增强型城市交通场景下的三维物体检测和活动分类的新框架。通过使用高架 LiDAR，我们获取了详细的三维点云数据，实现了精确的行人活动监测。为了克服城市数据匮乏问题，我们通过在 Blender 中模拟交通环境，创建了一个专门的数据集，便于针对性的模型训练。我们的方法采用了改进的基于点体素区域的卷积神经网络（PV-RCNN）进行稳健的三维检测和 PointNet 进行行人活动分类，显著促进了城市交通管理和公共卫生，为理解行人行为、促进更安全的城市环境提供了洞见。我们的双模型方法不仅提升了城市交通管理，还通过提供对行人行为的洞见，显著贡献于公共卫生和更安全的城市环境。