WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes
WatchDog: Real-time Vehicle Tracking on Geo-distributed Edge Nodes
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WatchDog：地理分布的边缘节点上的实时车辆跟踪
DOI：
10.1145/3549551
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Zheng Dong;Yan Lu;G. Tong;Yuanchao Shu;Shuai Wang;Weisong Shi
中科院分区：
文献类型：
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作者：
Zheng Dong;Yan Lu;G. Tong;Yuanchao Shu;Shuai Wang;Weisong Shi
车辆跟踪是智能城市视频分析的核心应用程序，由于交通摄像头数量的增加以及计算机视觉和机器学习的最新进展，车辆跟踪的部署比以往任何时候都要广泛。由于带宽、延迟和隐私问题的限制，跟踪任务更适合在靠近摄像头的边缘设备上运行。然而，边缘设备配备了固定数量的计算预算，这使得它们无法适应流量动态导致的时变和不平衡的跟踪工作负载。为了应对这一挑战，我们提出了WatchDog，一种充分利用道路网络中的边缘节点的实时车辆跟踪系统。WatchDog利用具有不同资源精度权衡的计算机视觉任务，并根据当前工作负载在边缘设备上明智地分解和调度跟踪任务，以最大限度地增加任务数量，同时确保每个边缘设备具有可证明的响应时间限制。使用真实的全市车辆轨迹数据集进行了广泛的评估，实现了出色的跟踪性能和实时保证。
Vehicle tracking, a core application to smart city video analytics, is becoming more widely deployed than ever before thanks to the increasing number of traffic cameras and recent advances in computer vision and machine-learning. Due to the constraints of bandwidth, latency, and privacy concerns, tracking tasks are more preferable to run on edge devices sitting close to the cameras. However, edge devices are provisioned with a fixed amount of computing budget, making them incompetent to adapt to time-varying and imbalanced tracking workloads caused by traffic dynamics. In coping with this challenge, we propose WatchDog, a real-time vehicle tracking system that fully utilizes edge nodes across the road network. WatchDog leverages computer vision tasks with different resource-accuracy tradeoffs, and decomposes and schedules tracking tasks judiciously across edge devices based on the current workload to maximize the number of tasks while ensuring a provable response time-bound at each edge device. Extensive evaluations have been conducted using real-world city-wide vehicle trajectory datasets, achieving exceptional tracking performance with a real-time guarantee.