论文标题
COSSINCE：边缘的协作跨摄像机视频分析
CONVINCE: Collaborative Cross-Camera Video Analytics at the Edge
论文作者
论文摘要
如今，摄像机被部署在密集的地方，以监视物理场所，例如城市，工业或农业场所。在当前系统中，每个摄像头节点单独发送给云服务器。但是，这种方法遇到了几个障碍，包括更高的计算成本，分析庞大数据的庞大带宽要求以及隐私问题。在密集的部署中，视频节点通常显示出显着的时空相关性。为了克服当前方法中的这些障碍，本文介绍了Corscince，这是一种将网络摄像机视为集体实体的新方法，该实体可以在摄像机之间进行协作视频分析管道。 Selvince的目标是1）通过利用相机之间的时空相关性来降低计算成本和带宽要求，以智能消除冗余帧，ii）提高视觉算法的准确性，通过启用相关相机之间的协作知识共享来提高视觉算法的精度。我们的结果表明，通过仅传输大约$ \ sim $ 25 \％的所有记录框架，COLCENCES可以实现$ \ sim $ 91 \％的对象识别精度。
Today, video cameras are deployed in dense for monitoring physical places e.g., city, industrial, or agricultural sites. In the current systems, each camera node sends its feed to a cloud server individually. However, this approach suffers from several hurdles including higher computation cost, large bandwidth requirement for analyzing the enormous data, and privacy concerns. In dense deployment, video nodes typically demonstrate a significant spatio-temporal correlation. To overcome these obstacles in current approaches, this paper introduces CONVINCE, a new approach to look at the network cameras as a collective entity that enables collaborative video analytics pipeline among cameras. CONVINCE aims at 1) reducing the computation cost and bandwidth requirements by leveraging spatio-temporal correlations among cameras in eliminating redundant frames intelligently, and ii) improving vision algorithms' accuracy by enabling collaborative knowledge sharing among relevant cameras. Our results demonstrate that CONVINCE achieves an object identification accuracy of $\sim$91\%, by transmitting only about $\sim$25\% of all the recorded frames.
