Parallelization of a new embedded application for automatic meteor detection

TL;DR

This paper details the parallelization of a computer vision application for meteor detection, optimized for low-power embedded systems like Raspberry Pi, achieving real-time processing of noisy video streams.

Contribution

It introduces a novel parallelization approach tailored for embedded meteor detection systems, enabling real-time processing on low-power hardware.

Findings

Achieves 42 frames per second on Raspberry Pi 4

Consumes only 6 Watts during operation

Demonstrates efficient parallelization techniques for embedded vision applications

Abstract

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.