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| Matabos, Marjolaine; Hoeberechts, Maia; Doya, Carol; Aguzzi, Jacopo; Nephin, Jessica; Reimchen, Thomas E.; Leaver, Steve; Marx, Roswitha M.; Albu, Alexandra Branzan; Fier, Ryan; Fernandez-arcaya, Ulla; Juniper, S. Kim. |
| 1.Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2.We compared the performance of three groups of humans and one computer vision algorithm in... |
| Tipo: Text |
Palavras-chave: Computer vision algorithms; Crowdsourcing; Deep-sea imagery; Digital Fishers; Fish counting; OceanNetworks Canada; Seafloor observatories; Underwater video. |
| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00369/47978/48006.pdf |
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