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Autonomous UAVs Wildlife Detection Using Thermal Imaging, Predictive Navigation and Computer Vision

机译：使用热成像，预测导航和计算机视觉的自主无人机野生动物检测

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摘要

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target's GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new waypoint for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.
机译：对野生动物和野生动物监测的无人航空车（无人机）使用无人驾驶航空公司（无人机）的利益增加了兴趣。本文介绍了一种新颖的系统，该系统使用预测动态应用，该动态应用程序将UAV放置在用户前方，具有低成本的热摄像机，一个小型车载计算机，该计算机从预定的高度识别目标动物的热签名，并传输该目标的GPS坐标。生成地图，使用设计以便轻松使用的GUI显示各种数据集和图形。本文介绍了用于检测动物的向下朝向相机的硬件和软件架构和概率模型。用于设计Kalman滤波器和基于Markov模型的预测算法的目标运动的行为动态用于放置UAV之前。几何概念和haversine公式应用于最大可能性案例，以便对用户的未来状态进行预测，从而为自主导航提供新的航路点。结果表明，该系统能够自主地定位从预定高度的动物，并生成示出用户前方的动物位置的地图。

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