Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection

TL;DR

This paper presents a novel framework combining LiDAR and IoT for precise pedestrian monitoring and activity detection in urban areas, aiming to improve safety and public health insights.

Contribution

It introduces a new dataset and a dual-model approach using PV-RCNN and PointNet for enhanced 3D detection and activity classification in urban traffic scenarios.

Findings

Improved accuracy in pedestrian activity recognition.

Effective use of simulated data for model training.

Enhanced urban safety and public health insights.

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.