SkyQuery：一种无人机视频传感平台
SkyQuery: An Aerial Drone Video Sensing Platform
摘要：
来自空中无人机，特别是小型多旋翼无人机的基于视频的传感可以为许多应用提供丰富的数据，包括交通分析（计算交通流量），精准农业（定期评估植物健康）和野生动物种群管理（估计种群大小）。然而，空中无人机视频传感应用必须处理范围惊人的任务：视频帧必须对齐，以便我们可以等同于出现在不同帧中的对象的坐标，必须分析视频数据以提取特定于应用的见解，并且必须计算无人机路线，以最大限度地提高新捕获视频的价值。为了应对这些挑战，我们构建了SkyQuery，这是一种新颖的空中无人机视频传感平台，它提供了一种富有表现力的高级程序设计语言，使用户能够直接开发复杂的长期运行的传感应用程序。SkyQuery结合了自上而下的空中无人机视频中快速视频帧对齐和小物体检测的新方法，以有效地执行具有不同视频分析工作流程和数据分布的应用程序，从而使应用程序开发人员能够专注于其特定应用程序的独特品质，而不是一般的视频处理，数据分析和无人机路由任务。我们使用SkyQuery在停车监控，行人活动地图和交通危险检测场景中进行各种案例研究，以证明我们的系统的通用性和有效性。
英文摘要：
Video-based sensing from aerial drones, especially small multirotor drones, can provide rich data for numerous applications, including traffic analysis (computing traffic flow volumes), precision agriculture (periodically evaluating plant health), and wildlife population management (estimating population sizes). However, aerial drone video sensing applications must handle a surprisingly wide range of tasks: video frames must be aligned so that we can equate coordinates of objects that appear in different frames, video data must be analyzed to extract application-specific insights, and drone routes must be computed that maximize the value of newly captured video. To address these challenges, we built SkyQuery, a novel aerial drone video sensing platform that provides an expressive, high-level programming language to make it straightforward for users to develop complex long-running sensing applications. SkyQuery combines novel methods for fast video frame alignment and detection of small objects in top-down aerial drone video to efficiently execute applications with diverse video analysis workflows and data distributions, thereby allowing application developers to focus on the unique qualities of their particular application rather than general video processing, data analysis, and drone routing tasks. We conduct diverse case studies using SkyQuery in parking monitoring, pedestrian activity mapping, and traffic hazard detection scenarios to demonstrate the generalizability and effectiveness of our system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
