  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.
