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| Conti, Stéphane; Roux, Philippe; Fauvel, Christian; Maurer, Benjamin; Demer, David. |
| A challenge for the aquaculture community has long been the development of harmless techniques for monitoring fish in a tank. Acoustic telemetry has been used to monitor fish swimming behavior, and passive acoustics have been used to monitor fish feeding, but new techniques are needed to monitor non-invasively their numbers and growth rates. Recently, it has been demonstrated that the acoustical total scattering cross section of fish swimming in a tank can be measured from multiple reverberation time series. These measurements have been used successfully to estimate the number of fish in a tank in laboratory conditions, and to characterize their acoustical signatures. Here, we introduce a novel method for acoustically monitoring fish numerical density and... |
| Tipo: Text |
Palavras-chave: Total scattering cross section; Fish counting; Growth rate; Remote monitoring; Fish behavior. |
| Ano: 2006 |
URL: http://archimer.ifremer.fr/doc/2006/publication-1121.pdf |
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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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