Breadcrumb
From Pixels to Protection: Artifical Intelligence in Baited Remote Underwater Video Analysis - Case Study from the Sandy Beach Surf Zone of Northern California
Presentation Year
2026
College or Department
College of Natural Resources & Sciences
Short Description of your Research or Creative Project (700 characters or less)
AI is being explored to analyze Baited Remote Underwater Video (BRUV) footage, which is currently processed manually. It is a time-consuming and costly task requiring trained technicians. This creates a major data bottleneck, as each hour of footage takes several hours to analyze. Despite this, BRUVs remain valuable for non-invasive monitoring, especially in sensitive habitats. Automating analysis with machine learning can reduce time and costs. This study tested untrained AI fish detection on BRUV footage from low-visibility surf zones in Northern California, establishing baseline data for improving non-destructive monitoring in dynamic environments.
Permission to Publish Work
Yes
Presentation File Upload
Primary Contact: First Name
Jazmyn
Primary Contact: Last Name
Sanchez
Primary Contact: Email
jir33@humboldt.edu
Primary Contact: I am a
Undergraduate Student
Primary Contact: Phone Number
909-234-2064
Indicate File Dimensions
3'x2'