用物理模型实时监控公共场所人流，防拥堵提体验
Massimo: Public Queue Monitoring and Management using Mass-Spring Model
- 基于质点弹簧模型模拟人流动态
- 通过视觉算法实现人群密度精准识别
- 适合城市交通与商业场所的智能调度
在公共空间中高效地进行队列控制与管理对于防止交通拥堵、提升顾客满意度至关重要。本文提出一种融合智能系统的技术路线，构建高效的公共区域队列管理系统。利用计算机视觉、机器学习及深度学习等技术，系统能够准确判断场所是否拥挤，并提供相应的干预建议。通过质点弹簧模型对人流行为建模，实现对人群流动的动态监测与调控，显著提升空间使用效率与用户体验。
原文摘要 · Abstract (English)
An efficient system of a queue control and regulation in public spaces is very important in order to avoid the traffic jams and to improve the customer satisfaction. This article offers a detailed road map based on a merger of intelligent systems and creating an efficient systems of queues in public places. Through the utilization of different technologies i.e. computer vision, machine learning algorithms, deep learning our system provide accurate information about the place is crowded or not and the necessary efforts to be taken.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。