LiDAR and Image Filtering and Fusion Techniques for Real-Time Crowd Monitoring System

This research explores an affordable and high-precision crowd-monitoring system of integrating data from 2D LiDAR and images from the camera through LiDAR scan data and image fusion. The novelty of the research is to achieve 3-D scanning by using 2D LiDAR, which is controlled by a servo-controlled tilting mechanism to obtain multiple scan data from different elevation angles, for simulating 3D scanning operation through overlapping of multiple scanning results according to its elevation angles and performs image, 2D point cloud data fusion for human detection and distance measurement for crowd monitoring purposes. The proposed techniques enhance 2D LiDAR detection, enabling detailed scanning at lower cost and complexity. The system combines LiDAR measurements with camera imagery through proposed filtering and fusion algorithms which are implemented on a novel servo-controlled swinging platform, essential for accurate real-time tracking in enclosed crowded areas. The outcomes of the research show that the proposed crowd-monitoring system can accurately localize an individual using LiDAR scan data in terms ofhis/her distance and angle from an image with a bounding box aiming to classify the detected object as a human being with high accuracy by using the proposed filtering and fusion techniques.