Unmanned aerial vehicles assist in maritime search and rescue missions by flying over large search areas to autonomously search for objects or people. Reliably detecting objects of interest requires fast models to employ on embedded hardware. Moreover, with increasing distance to the ground station only part of the video data can be transmitted. In this work, we consider the problem of finding meaningful region of interest proposals in a video stream on an embedded GPU. Current object or anomaly detectors are not suitable due to their slow speed, especially on limited hardware and for large image resolutions. Lastly, objects of interest, such as pieces of wreckage, are often not known a priori. Therefore, we propose an end-to-end future frame prediction model running in real-time on embedded GPUs to generate region proposals. We analyze its performance on large-scale maritime data sets and demonstrate its benefits over traditional and modern methods.

翻译：无人机通过飞越广阔搜索区域自主搜索人员或物体，协助执行海上搜救任务。可靠检测目标物体需要能在嵌入式硬件上快速运行的模型。此外，随着与地面站距离的增加，仅能传输部分视频数据。本研究探讨在嵌入式GPU上从视频流中寻找有意义感兴趣区域提议的问题。现有目标或异常检测器因运行速度慢，尤其在有限硬件资源及大分辨率图像条件下难以适用。此外，诸如残骸碎片等目标物体通常无法预先获知。为此，我们提出一种在嵌入式GPU上实时运行的端到端未来帧预测模型，用于生成区域提议。通过在大规模海上数据集上分析其性能，我们证明了该方法相较于传统与现代方法的优势。