@Conference{Kandeepan2022,
  author       = {Kandeepan, Mathuran and Ciocan, Clara and Millet, Maxime and Bouyer, Manuel and Cassagne, Adrien and Lacassagne, Lionel},
  booktitle    = {Journ{\'e}e AFF3CT},
  title        = {Fast Meteor Detection Toolbox},
  year         = {2022},
  month        = Nov,
  note         = {Journ{\'e}e AFF3CT. Poster.},
  organization = {{Centre Inria de l'Universit{\'e} de Bordeaux}},
  abstract     = {Detection and characterization of meteoroids and space debris that enter into our atmosphere is one of the main concerns of astronomers. This work presents a computer vision application to detect meteors from video sequences. This application is designed to be embedded in satellites, in weather balloons or for airborne observation campaigns. The system runs on CPUs and is robust to non-stabilized cameras and noisy video sequences. It is evaluated on the 2022 Tau-Herculids video sequence where an overall tracking rate of 80.4\% is obtained. All the visible meteors are detected. Experiment results demonstrate that the system runs efficiently with limited power thanks to the use of multithreading techniques (pipeline and fork-join parallelism). For example, on the 2022 Tau-Herculids HD video sequence, the system reaches 30 FPS on the Raspberry Pi 4 while the instant power is only 3.8 Watts.},
  doi          = {10.13140/RG.2.2.12222.36161},
  hal_id       = {hal-03954992},
  hal_version  = {v1},
  keywords     = {computer vision, Multithreading, meteor cluster, embedded system, pipeline parallelism, meteor tracking, tasks graph, data flow},
  url_Slides   = {https://hal.science/hal-03954992/file/poster_fmdt.pdf},
  url_Link     = {https://hal.science/hal-03954992},
}

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