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.
翻译:从空中无人驾驶飞机,特别是小型多机器人无人驾驶飞机的视频感测,可以为多种应用提供丰富的数据,包括交通分析(计算流量数量)、精密农业(定期评估植物健康)和野生生物人口管理(估计人口规模),然而,空中无人机视频感测应用必须处理出令人惊讶的广泛任务:视频框架必须加以调整,以便我们能够将不同框架中出现的物体的坐标等同起来,必须分析视频数据以提取具体应用的洞察力,而且必须计算无人机路线以尽量扩大新捕获的视频的价值。为了应对这些挑战,我们建立了SkyQuery,这是一个新型的空中无人机视频感测平台,它提供了直观、高层次的编程语言,使用户能够直接开发复杂的长期遥感应用。SkyQuery将快速视频框架调整和探测上下空中无人机视频视频中小物体的新方法结合起来,以便有效地执行各种视频分析工作流程和数据传播的应用,从而使应用程序开发者能够集中关注其特定应用的独特质量,而不是一般视频处理、数据分析、数据分析和无人机测路程任务。我们利用SkyQuer系统进行各种风险探测和测算活动,我们进行一般的可探测和测图。