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利用 Tello EDU 无人机进行实时人体运动跟踪。

Real-Time Human Motion Tracking by Tello EDU Drone.

机构信息

Department of Electrical Engineering, Faculty of Engineering, Burapha University Chonburi Campus, Chonburi 20131, Thailand.

出版信息

Sensors (Basel). 2023 Jan 12;23(2):897. doi: 10.3390/s23020897.

DOI:10.3390/s23020897
PMID:36679699
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9860987/
Abstract

Human movement tracking is useful in a variety of areas, such as search-and-rescue activities. CCTV and IP cameras are popular as front-end sensors for tracking human motion; however, they are stationary and have limited applicability in hard-to-reach places, such as those where disasters have occurred. Using a drone to discover a person is challenging and requires an innovative approach. In this paper, we aim to present the design and implementation of a human motion tracking method using a Tello EDU drone. The design methodology is carried out in four steps: (1) control panel design; (2) human motion tracking algorithm; (3) notification systems; and (4) communication and distance extension. Intensive experimental results show that the drone implemented by the proposed algorithm performs well in tracking a human at a distance of 2-10 m moving at a speed of 2 m/s. In an experimental field of the size 95×35m2, the drone tracked human motion throughout a whole day, with the best tracking results observed in the morning. The drone was controlled from a laptop using a Wi-Fi router with a maximum horizontal tracking distance of 84.30 m and maximum vertical distance of 13.40 m. The experiment showed an accuracy rate for human movement detection between 96.67 and 100%.

摘要

人体运动跟踪在许多领域都很有用，例如搜索和救援活动。闭路电视和 IP 摄像机作为前端传感器，常用于跟踪人体运动；然而，它们是固定的，在难以到达的地方，如发生灾难的地方，适用性有限。使用无人机来发现人是具有挑战性的，需要创新的方法。在本文中，我们旨在提出一种使用 Tello EDU 无人机进行人体运动跟踪的设计和实现方法。设计方法分四步进行：（1）控制面板设计；（2）人体运动跟踪算法；（3）通知系统；（4）通信和距离扩展。大量实验结果表明，所提出算法实现的无人机在跟踪距离为 2-10 米、速度为 2 米/秒的人体时表现良好。在 95×35m2 的实验场地中，无人机全天跟踪人体运动，早上的跟踪效果最佳。无人机通过笔记本电脑使用 Wi-Fi 路由器进行控制，最大水平跟踪距离为 84.30 米，最大垂直跟踪距离为 13.40 米。实验表明，人体运动检测的准确率在 96.67%至 100%之间。

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