# Parallelization of a new embedded application for automatic meteor detection

arXiv: https://arxiv.org/abs/2307.10632 · HTML: https://mlsanity.com/paper/2307.10632/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [A DSEL for High Throughput and Low Latency Software-Defined Radio on Multicore CPUs](https://mlsanity.com/paper/2206.06147/) (2022)

## Abstract (arXiv)

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.