Solution > Application of LiDAR in people flow detection
Application of LiDAR in people flow detection
Emergency response coordination, resource optimization, and full-scenario coverage
Medium distance light pulse series
Industry & Project Overview
With rapid urbanization, pedestrian traffic in high-density areas such as shopping malls, office buildings, schools, and scenic spots continues to rise, posing significant challenges to traditional evacuation methods in emergencies like fires and earthquakes. To optimize emergency rescue efficiency, intelligent pedestrian flow monitoring systems utilize multi-dimensional sensing technologies (such as 3D visual analysis and heat map tracking) to acquire accurate real-time pedestrian flow and distribution data for various areas. This system dynamically monitors passenger density in key passageways such as elevators and staircases, combining AI algorithms to analyze pedestrian flow trends and generate visualized heat maps. When a disaster occurs, this real-time data assists command centers in quickly developing rescue plans, rationally allocating emergency resources such as fire and medical personnel, prioritizing evacuation efficiency in high-density areas, increasing rescue response speed by over 40%, and minimizing casualties.
Application Scenarios
- During a disaster, crowd flow data is pushed to the rescue command platform within seconds.
Technical Solution
- During a disaster, crowd flow data is pushed to the rescue command platform within seconds.
- This increases the efficiency of rescue force allocation by 50% and doubles the utilization rate of the golden rescue time.
Customer Value
- Supports real-time monitoring of various high-density locations such as shopping malls, schools, and transportation hubs.
- AI generates a real-time visualization model of people distribution to pinpoint congestion risk points.
Technical Highlights
- Employing multi-sensor fusion technology, the accuracy rate of people counting is ≥98%.
Application Examples
Website
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Company Address:
No. 777, Kejian Road, Jiangning District, Nanjing, Jiangsu, China
