Vision-Based Target Detection and Localization via a Team of Cooperative UAV and UGVs
Vision-Based Target Detection and Localization via a Team of Cooperative UAV and UGVs
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DOI：
10.1109/tsmc.2015.2491878
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发表时间：
2016-07-01
影响因子：
8.7
通讯作者：
Son, Young-Jun
中科院分区：
文献类型：
--
作者：
Minaeian, Sara;Liu, Jian;Son, Young-Jun
无人驾驶车辆（UV）在自主监控场景中发挥着关键作用。这些无人机在执行自主巡逻任务时所需的主要任务是实时检测目标并找到它们的位置。本文提出了一种新的基于视觉的目标检测和定位系统，利用不同的能力的UV作为一个合作的团队。本文考虑的场景是一个无人驾驶飞行器（UAV）和多个无人驾驶地面车辆（UGV）跟踪和控制边境地区的人群。一个定制的运动检测算法被应用于从安装在无人机上的移动摄像机跟踪人群。由于无人机的分辨率较低，检测范围较广，具有较高的分辨率和保真度的UGV被用来作为个人的人体检测器，以及移动地标定位检测到的人群与未知的独立移动模式在每个时间点。本文提出的无人机定位算法，然后转换的人群的图像位置到他们的现实世界中的位置，使用透视变换。还提出了一种UGV的经验法则定位方法，该方法估计检测到的个体的地理位置。此外，一个基于代理的仿真模型的系统验证，与不同的参数，如飞行高度，地标的数量，和地标分配方法。在本文中考虑的性能指标是估计的位置和模拟的人群的地理航路点之间的平均欧几里德距离。实验结果表明，所提出的框架的有效性，自主监视的UV。
Unmanned vehicles (UVs) play a key role in autonomous surveillance scenarios. A major task needed by these UVs in undertaking autonomous patrol missions is to detect the targets and find their locations in real-time. In this paper, a new vision-based target detection and localization system is presented to make use of different capabilities of UVs as a cooperative team. The scenario considered in this paper is a team of an unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs) tracking and controlling crowds on a border area. A customized motion detection algorithm is applied to follow the crowd from the moving camera mounted on the UAV. Due to UAVs lower resolution and broader detection range, UGVs with higher resolution and fidelity are used as the individual human detectors, as well as moving landmarks to localize the detected crowds with unknown independently moving patterns at each time point. The UAVs localization algorithm, proposed in this paper, then converts the crowds' image locations into their real-world positions, using perspective transformation. A rule-of-thumb localization method by a UGV is also presented, which estimates the geographic locations of the detected individuals. Moreover, an agent-based simulation model is developed for system verification, with different parameters, such as flight altitude, number of landmarks, and landmark assignment method. The performance measure considered in this paper is the average Euclidean distance between the estimated locations and simulated geographic waypoints of the crowd. Experimental results demonstrate the effectiveness of the proposed framework for autonomous surveillance by UVs.