This article presents the methods used to parallelize a new computer vision
application. The system is able to automatically detect meteor from
non-stabilized cameras and noisy video sequences. The application is designed
to be embedded in weather balloons or for airborne observation campaigns. Thus,
the final target is a low power system-on-chip (< 10 Watts) while the software
needs to compute a stream of frames in real-time (> 25 frames per second). For
this, first the application is split in a tasks graph, then different
parallelization techniques are applied. Experiment results demonstrate the
efficiency of the parallelization methods. For instance, on the Raspberry Pi 4
and on a HD video sequence, the processing chain reaches 42 frames per second
while it only consumes 6 Watts.

该研究介绍了一种用于将计算机视觉应用程序并行化的方法，该应用程序能够自动从非稳定的相机和嘈杂的视频序列中检测流星。该应用程序设计为嵌入气象气球或用于空中观测任务，因此最终目标是一个低功率的片上系统（小于 10 瓦），而软件需要实时计算连续的图像帧（大于每秒 25 帧）。首先，将应用程序划分为任务图，然后应用不同的并行化技术。实验结果表明并行化方法的高效性。例如，在 Raspberry Pi 4 上及高清视频序列上，处理链路达到每秒 42 帧，仅消耗 6 瓦。