Heartbeat of a nest
Abstract
We present a scalable end-to-end system for vision-based monitoring of natural environments, and illustrate its use for the analysis of avian nesting cycles. Our system enables automated analysis of thousands of images, where manual processing would be infeasible. We automate the analysis of raw imaging data using statistics that are tailored to the task of interest. These “features” are a representation to be fed to classifiers that exploit spatial and temporal consistencies. Our testbed can detect the presence or absence of a bird with an accuracy of 82%, count eggs with an accuracy of 84%, and detect the inception of the nesting stage within a day. Our results demonstrate the challenges and potential benefits of using imagers as biological sensors. An exploration of system performance under varying image resolution and frame rate suggest that an in situ adaptive vision system is technically feasible.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 01, 2010
- Source ID
- 10.1145/1754414.1754415
Entities
People
- Deborah Estrin
- John Hicks
- Michael P. Hamilton
- Mohammad Rahimi
- Sharon Coe
- Shaun Ahmadian
- Stefano Soatto
- Teresa Ko
Organizations
- National Science Foundation
- Office of Naval Research
- United States Forest Service
- University of California, Berkeley
- University of California, Los Angeles