Tilo Burghardt, Ben Hughes
Recognising individuals repeatedly over time is a basic requirement for field-based ecology and related marine sciences. In scenarios where photographic capture is feasible and animals are visually unique, biometric computer vision offers a non-invasive identification paradigm.
In this line of work we developed the first fully automated biometric ID system for individual animals based on visual body contours. We applied the techniques to great white shark identification. The work was selected as one of the top 10 BMVC’15 papers and subsequently published in IJCV. The work was collaborative with NGO SaveOurSeas Foundation (SoSF) who employed Ben Hughes to extend and apply this work. The system is now being exploited at large scale by SoSF.
B Hughes, T Burghardt. Automated Visual Fin Identification of Individual Great White Sharks. International Journal of Computer Vision (IJCV), Vol 122, No 3, pp. 542-557, May 2017. (DOI:10.1007/s11263-016-0961-y), (Dataset FinsScholl2456)
B Hughes, T Burghardt. Automated Identification of Individual Great White Sharks from Unrestricted Fin Imagery. 26th British Machine Vision Conference (BMVC), pp. 92.1-92.14, ISBN 1-901725-53-7, BMVA Press, September 2015. (DOI:10.5244/C.29.92), (Dataset FinsScholl2456)
B Hughes, T Burghardt. Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery. Machine Vision of Animals and their Behaviour (MVAB), Workshop at BMVC, pp. 8.1-8.8. BMVA Press, September 2015. (DOI:10.5244/CW.29.MVAB.8), (Dataset FinsScholl2456)