Parallelization of a new embedded application for automatic meteor detection
This enables automated meteor detection for weather balloons or airborne campaigns, but it is incremental as it focuses on parallelizing an existing application.
The authors tackled the problem of real-time meteor detection from non-stabilized, noisy video streams on low-power embedded systems, achieving 42 frames per second with 6 Watts consumption on a Raspberry Pi 4.
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.