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.

翻译：通过计算机视觉检测混合关键事件具有挑战性，因为需要上下文理解以准确评估事件的关键性。混合关键事件（如不同严重程度的火灾或交通事故）需要能够解读上下文以触发适当响应的自适应系统。本文通过为智慧城市应用提出一种通用检测系统来应对这些挑战，提供了在交通和火灾检测场景中经过测试的解决方案。我们的贡献包括对检测需求的分析以及开发适用于多样化应用的自适应系统，从而推进智慧城市的自动化监控。