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Presentation Type
Presentation
Full Name of Faculty Mentor
Xiangxiong Kong, Physics and Engineering Science
Other Mentors
Erin Burge, Marine Science
Major
Engineering Science
Presentation Abstract
According to the National Ocean Service, only 5% of the oceans have been explored. New behaviors of underwater species call to question how much we even know about what little we have discovered. With so much left to discover, the call for novel methods of marine observation is an urgent research need. The accuracy and availability of the automated observation option through computer vision and image processing have shown great potential to assist marine observation. In this study, we proposed a computer vision-based methodology to program a system to extract, identify, and report fish movements that may not be easily seen by human eyes. Our method has been validated through the MATLAB computer vision toolbox using field images taken from video footage of a live-streaming underwater camera installed beneath Frying Pan Tower in North Carolina. Results indicated our method can successfully identify and track movements of fishes from the video frames.
Location
Room 2 (BRTH 112)
Start Date
12-4-2022 3:00 PM
End Date
12-4-2022 3:20 PM
Disciplines
Engineering Science and Materials
Recommended Citation
Rhodes, James, "Fish identification through video motion tracking from a publicly available live-streaming camera" (2022). Undergraduate Research Competition. 68.
https://digitalcommons.coastal.edu/ugrc/2022/fullconference/68
Fish identification through video motion tracking from a publicly available live-streaming camera
Room 2 (BRTH 112)
According to the National Ocean Service, only 5% of the oceans have been explored. New behaviors of underwater species call to question how much we even know about what little we have discovered. With so much left to discover, the call for novel methods of marine observation is an urgent research need. The accuracy and availability of the automated observation option through computer vision and image processing have shown great potential to assist marine observation. In this study, we proposed a computer vision-based methodology to program a system to extract, identify, and report fish movements that may not be easily seen by human eyes. Our method has been validated through the MATLAB computer vision toolbox using field images taken from video footage of a live-streaming underwater camera installed beneath Frying Pan Tower in North Carolina. Results indicated our method can successfully identify and track movements of fishes from the video frames.