@article{fdi:010071324,
  title = {{W}avelet-based optical flow estimation of instant surface currents from shore-based and {UAV} videos},
  author = {{D}erian, {P}. and {A}lmar, {R}afa{\¨e}l},
  editor = {},
  language = {{ENG}},
  abstract = {{I}nstant fields of surface current are retrieved from shore-based and unmanned aerial vehicle videos by an optical flow ({OF}) method named " {T}yphoon." {T}his computer vision algorithm estimates dense 2-{D} 2-component velocity fields from the observable motion of foam patterns in the surf zone. {D}espite challenging image data resolution and quality, comparison of {OF} surface current estimates with measurements by an acoustic {D}oppler velocimeter reveals its ability to capture both wave-towave fluctuations and low-frequency variations. {T}he method is also successfully applied to the monitoring of a "flash rip" event. {T}his paper shows clearly the high potential of this method in the nearshore, where the rapid development of webcams and drones offers a large number of applications for swimming and surfing safety, engineering and naval security, and research purpose, by providing quantitative information.},
  keywords = {{G}eophysical inverse problems ; image motion analysis ; remote sensing ; {R}ip currents ; sea coast ; sea surface ; unmanned aerial vehicles ({UAV}s) ; wavelet transforms ; {BENIN} ; {GUINEE} {GOLFE} ; {GRAND} {POPO}},
  booktitle = {},
  journal = {{IEEE} {T}ransactions on {G}eoscience and {R}emote {S}ensing},
  volume = {55},
  numero = {10},
  pages = {5790--5797},
  ISSN = {0196-2892},
  year = {2017},
  DOI = {10.1109/tgrs.2017.2714202},
  URL = {https://www.documentation.ird.fr/hor/fdi:010071324},
}