用视觉与边缘计算实时监测城市道路，预防交通事故。
Real-time Object and Event Detection Service through Computer Vision and Edge Computing
- 在边缘设备部署视觉算法，实现近实时检测与追踪。
- 可识别车辆、行人、自行车并预测碰撞风险，准确率高。
- 适合智慧城市建设者与交通安全管理研究人员参考。
世界卫生组织指出，全球每年道路交通事故造成的经济损失约5180亿美元，占多数国家国内生产总值的3%。城市中大多数致命事故涉及弱势道路使用者（VRUs）。智慧城市环境通过先进传感器、大规模数据集、机器学习模型、通信系统及边缘计算等技术，提供创新解决方案。本文提出一种基于计算机视觉（CV）与边缘计算的城市道路监控与安全系统策略与实现方案。在阿威罗科技城生活实验室（ATCLL）测试平台上，通过部署视觉算法和跟踪机制，利用城市监控摄像头实现了对车辆、行人、自行车的精准检测与追踪，并能近实时预测道路状态、物体间距离，推断碰撞事件以预防事故发生，取得显著成效。
原文摘要 · Abstract (English)
The World Health Organization suggests that road traffic crashes cost approximately 518 billion dollars globally each year, which accounts for 3% of the gross domestic product for most countries. Most fatal road accidents in urban areas involve Vulnerable Road Users (VRUs). Smart cities environments present innovative approaches to combat accidents involving cutting-edge technologies, that include advanced sensors, extensive datasets, Machine Learning (ML) models, communication systems, and edge computing. This paper proposes a strategy and an implementation of a system for road monitoring and safety for smart cities, based on Computer Vision (CV) and edge computing. Promising results were obtained by implementing vision algorithms and tracking using surveillance cameras, that are part of a Smart City testbed, the Aveiro Tech City Living Lab (ATCLL). The algorithm accurately detects and tracks cars, pedestrians, and bicycles, while predicting the road state, the distance between moving objects, and inferring on collision events to prevent collisions, in near real-time.
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