Abstract
Detecting mixed-critical events through computer vision is challenging due to the need for contextual understanding to assess event criticality accurately. Mixed critical events, such as fires of varying severity or traffic incidents, demand adaptable systems that can interpret context to trigger appropriate responses. This paper addresses these challenges by proposing a versatile detection system for smart city applications, offering a solution tested across traffic and fire detection scenarios. Our contributions include an analysis of detection requirements and the development of a system adaptable to diverse applications, advancing automated surveillance for smart cities.
Abstract (translated)
通过计算机视觉检测混合关键事件颇具挑战性，因为需要对情境进行理解以准确评估事件的严重程度。例如不同严重程度的火灾或交通事故等混合关键事件要求系统具有可适应性，能够解读情境并触发适当的响应。本文针对这些挑战提出了一个适用于智慧城市应用的多功能检测系统，并在交通和火灾检测场景中进行了测试。我们的贡献包括对检测需求的分析以及开发了一个可以应用于多种场景的系统，从而推动了智慧城市的自动化监控发展。
URL
https://arxiv.org/abs/2411.15773
