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首页> 外文期刊>Proceedings of the IEEE >Large-Scale Situation Awareness With Camera Networks and Multimodal Sensing
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Large-Scale Situation Awareness With Camera Networks and Multimodal Sensing

机译：摄像机网络和多模式传感的大规模态势感知

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摘要

Sensors of various modalities and capabilities, especially cameras, have become ubiquitous in our environment. Their intended use is wide ranging and encompasses surveillance, transportation, entertainment, education, healthcare, emergency response, disaster recovery, and the like. Technological advances and the low cost of such sensors enable deployment of large-scale camera networks in large metropolises such as London and New York. Multimedia algorithms for analyzing and drawing inferences from video and audio have also matured tremendously in recent times. Despite all these advances, large-scale reliable systems for media-rich sensor-based applications, often classified as situation-awareness applications, are yet to become commonplace. Why is that? There are several forces at work here. First, the system abstractions are just not at the right level for quickly prototyping such applications on a large scale. Second, while Moore's law has held true for predicting the growth of processing power, the volume of data that applications are called upon to handle is growing similarly, if not faster. Enormous amount of sensing data is continually generated for real-time analysis in such applications. Further, due to the very nature of the application domain, there are dynamic and demanding resource requirements for such analyses. The lack of right set of abstractions for programing such applications coupled with their data-intensive nature have hitherto made realizing reliable large-scale situation-awareness applications difficult. Incidentally, situation awareness is a very popular but ill-defined research area that has attracted researchers from many different fields. In this paper, we adopt a strong systems perspective and consider the components that are essential in realizing a fully functional situation-awareness system.
机译：在我们的环境中，各种形式和功能的传感器（尤其是相机）已经无处不在。它们的预期用途广泛，包括监视，运输，娱乐，教育，医疗保健，紧急响应，灾难恢复等。这种传感器的技术进步和低成本使其能够在诸如伦敦和纽约这样的大都市中部署大型摄像机网络。近年来，用于分析和绘制来自视频和音频的推断的多媒体算法也已经日趋成熟。尽管取得了所有这些进步，但用于基于媒体的基于传感器的应用程序的大规模可靠系统（通常被称为情境感知应用程序）尚未普及。这是为什么？这里有几种力量在起作用。首先，系统抽象还没有达到正确的水平，无法快速大规模地对此类应用进行原型设计。其次，尽管摩尔定律对于预测处理能力的增长一直成立，但被要求处理的应用程序的数据量却在以同样的速度增长，即使不是更快。不断产生大量的传感数据，以便在此类应用中进行实时分析。此外，由于应用程序域的性质，对此类分析存在动态且苛刻的资源需求。迄今为止，缺乏用于对这样的应用进行编程的正确的抽象集，加上它们的数据密集型特性，使得实现可靠的大规模态势感知应用变得困难。顺便说一句，态势感知是一个非常流行但定义不清的研究领域，吸引了来自许多不同领域的研究人员。在本文中，我们采用强大的系统视角，并考虑了在实现功能全面的态势感知系统中必不可少的组件。

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