Spatula: Efficient cross-camera video analytics on large camera networks
Spatula: Efficient cross-camera video analytics on large camera networks
复制标题
DOI：
10.1109/sec50012.2020.00016
复制
发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Samvit Jain;Xun Zhang;Yuhao Zhou;Ganesh Ananthanarayanan;Junchen Jiang;Yuanchao Shu;P. Bahl;Joseph Gonzalez
中科院分区：
文献类型：
--
作者：
Samvit Jain;Xun Zhang;Yuhao Zhou;Ganesh Ananthanarayanan;Junchen Jiang;Yuanchao Shu;P. Bahl;Joseph Gonzalez
摄像机被大规模部署，目的是通过实时视频的摄像机网络搜索和跟踪感兴趣的对象（例如，嫌疑人）。这种跨摄像头的分析是数据和计算密集型的，其成本随着摄像头数量和时间的增长而增长。我们介绍了Spatula，这是一个经济高效的系统，通过利用空间和时间的跨相机相关性，可以将边缘计算盒上的跨相机分析扩展到大型相机网络。虽然这种相关性已经在计算机视觉社区中使用，但Spatula通过修剪查询标识的搜索空间（例如，忽略与查询标识当前位置不相关的帧）来大幅降低通信和计算成本。Spatula提供了第一个系统基板，可以在其上构建跨相机分析应用程序，以有效地利用大型相机部署中丰富的跨相机相关性。Spatula在8个摄像头的数据集上减少了8.3倍的计算负荷，在两个具有数百个摄像头的数据集上减少了23倍至86倍的计算负荷（从真实的车辆/行人轨迹模拟）。我们还在5台AWS DeepLens相机的测试平台上实现了Spatula。
Cameras are deployed at scale with the purpose of searching and tracking objects of interest (e.g., a suspected person) through the camera network on live videos. Such cross-camera analytics is data and compute intensive, whose costs grow with the number of cameras and time. We present Spatula, a cost-efficient system that enables scaling cross-camera analytics on edge compute boxes to large camera networks by leveraging the spatial and temporal cross-camera correlations. While such correlations have been used in computer vision community, Spatula uses them to drastically reduce the communication and computation costs by pruning search space of a query identity (e.g., ignoring frames not correlated with the query identity’s current position). Spatula provides the first system substrate on which cross-camera analytics applications can be built to efficiently harness the cross-camera correlations that are abundant in large camera deployments. Spatula reduces compute load by $8.3\times$ on an 8-camera dataset, and by $23\times-86\times$ on two datasets with hundreds of cameras (simulated from real vehicle/pedestrian traces). We have also implemented Spatula on a testbed of 5 AWS DeepLens cameras.