Scaling Video Analytics Systems to Large Camera Deployments
Scaling Video Analytics Systems to Large Camera Deployments
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DOI：
10.1145/3301293.3302366
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Samvit Jain;Ganesh Ananthanarayanan;Junchen Jiang;Yuanchao Shu;Joseph E. Gonzalez
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文献类型：
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作者：
Samvit Jain;Ganesh Ananthanarayanan;Junchen Jiang;Yuanchao Shu;Joseph E. Gonzalez
在计算机视觉的进步和摄像头硬件成本下降的推动下，组织正在部署视频摄像头，以实现对其物理场所的空间监控。然而，将视频分析扩展到大规模摄像头部署带来了一个新的挑战，因为计算成本与摄像头馈送的数量成比例增长。这篇论文是由一个简单的问题驱动的：我们能否以这样一种方式扩展视频分析，即随着我们部署更多的摄像头，成本呈次线性增长，甚至保持不变，同时推理准确性保持稳定，甚至提高。我们认为答案是肯定的。我们的主要观察结果是，来自广域摄像机部署的视频源在空间和时间上都表现出显著的内容相关性（例如，与其他地理位置接近的源）。可以利用这些时空相关性来显著减小推理搜索空间的大小，从而降低多摄像机视频分析中的工作量和误报率。通过讨论用例和技术挑战，我们提出了将视频分析扩展到大型摄像机网络的路线图，并概述了其实现计划。
Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale video analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera video analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling video analytics to large camera networks, and outline a plan for its realization.