Coral-Pie: A Geo-Distributed Edge-compute Solution for Space-Time Vehicle Tracking
Coral-Pie: A Geo-Distributed Edge-compute Solution for Space-Time Vehicle Tracking
复制标题
DOI：
10.1145/3423211.3425686
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Zhuangdi Xu;Harshil S. Shah;U. Ramachandran
中科院分区：
文献类型：
--
作者：
Zhuangdi Xu;Harshil S. Shah;U. Ramachandran
我们提出了一种分布式系统架构，该架构通过设计可扩展，用于在视频摄取时间进行跨摄像头车辆跟踪，称为Coral-Pie。为了满足每个相机及时处理每帧的延迟界限，我们为每个相机关联了专用的低成本计算资源，该资源由两个树莓派3B+和一个珊瑚加速器（EdgeTpu）组成。端到端系统生成并存储在图形数据库中，方便查询。我们使用Cloud-Edge-Device连续体来适当地放置分布式系统架构的组件。使用在每个摄像机的每一帧上需要进行连续处理的子任务的定时配置文件，我们将处理的元素映射到与每个摄像机相关的计算资源上。概念验证系统的性能评估是使用来自五个校园摄像机的实时流进行的。评估包括微基准测试和应用级研究。在现场摄像机控制实验的基础上，进行了基于仿真的研究，以展示系统的自愈特性和系统的可扩展性。
We present a distributed system architecture which is scalable by design for cross-camera vehicle tracking at video ingestion time dubbed Coral-Pie. To meet the latency bounds for timely processing of every frame at each camera, we associate dedicated low-cost computational resource for each camera, which consists of two Raspberry Pi 3B+'s and one Coral Accelerator (EdgeTpu). The end-to-end system generates and stores the tracks in a graph database for easy querying. We use the Cloud-Edge-Device continuum to appropriately place the components of the distributed system architecture. Using the timing profiles of the sub-tasks involved in the continuous processing that needs to happen on every frame in each camera, we map the elements of the processing onto the computational resource associated with each camera. Performance evaluation of the proof-of-concept system is conducted using live streams from five campus cameras. The evaluation includes microbenchmarks as well as application level studies. The controlled experiments using live cameras are augmented with a simulation-based study to show the self-healing property of the system and the system scalability.