Automated video identification of marine species (AVIMS) - new application: report
A commissioned report on the development of a web-based computer application for machine learning-based (semi-)automated analysis of underwater video footage obtained during the monitoring of aquatic environments.
References
Bewley, A., Ge, Z., Ott, L., Ramos, F. and Upcroft, B. 2016. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), 3464-3468.
Blowers, S., Evans, J. and McNally, K. 2020. Automated Identification of Fish and Other Aquatic Life in Underwater Video. In: Scottish Marine and Freshwater Science, Vol 11 No 18. 62pp. DOI: 10.7489/12333-1.
French, G., Mackiewicz, M., Fisher, M., Challiss, M., Knight, P., Robinson, B. and Bloomfield, A. 2018. JellyMonitor: automated detection of jellyfish in sonar images using neural networks. In: IEEE International Conference on Signal Processing (ICSP), IEEE, 406-412.
French, G., Mackiewicz, M., Fisher, M., Holah, H., Kilburn, R., Campbell, N. and Needle, C. 2020. Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards. ICES Journal of Marine Science, 77(4), 1340-1353.
Gorpincenko, A., French, G., Knight, P., Challiss, M. and Mackiewicz, M. 2020. Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms. IEEE Sensors Journal.
Contact
Email: craig.robinson@gov.scot
There is a problem
Thanks for your feedback