Abstract
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.
Abstract (translated)
利用光探测和测距（LiDAR）和物联网（IoT）技术在智慧城市和安全行人环境中，集成LiDAR和IoT技术为公共健康信息学带来了变革性的机会。本文提出了一种利用这些技术在城市交通场景中增强3D物体检测和活动分类的新颖框架。通过采用升高的LiDAR技术，我们获得了详细的三维点云数据，实现了对行人活动的精确监测。为了克服城市数据的稀缺性，我们在Blender中通过模拟交通环境创建了一个专门的数据集，从而实现针对模型的定向训练。我们采用了一种基于点体素区域卷积神经网络（PV-RCNN）的修改后的检测和PointNet进行分类的方法，显著提高了智能交通管理和公共卫生水平，通过提供对行人行为的见解，促进了更安全的城市环境。我们双模型方法不仅提高了智能交通管理，而且对公共卫生也做出了显著贡献，提供了对行人行为的见解，促进了更安全的城市环境。
URL
https://arxiv.org/abs/2404.10978
