WTB is a research project that investigates the design and implementation of an end-to-end, distributed,
Internet of Things (IoT) system for wildlife monitoring. WTB
- Is a multi-tier (public/private cloud, edge, sensing) system that
integrates recent advances in machine learning based image processing to
automatically classify animals in images from remote, motion-detection camera traps.
- Uses non-local, resource-rich, public/private cloud systems to train machine learning models,
and ``in-the-field,'' resource-constrained edge systems to perform classification near the IoT sensing
- Trains models using only empty images synthesized with randomly placed animal images from Google Images.
- Relieves scientists and citizen scientists of the burden of manual image
classification and saves time and bandwidth for image transfer off-site by
automatically filtering the images on-site based on characteristics of interest.
- Is deployed at the UCSB Sedgwick Reserve, a 6000 acre site for environmental
research and used to aggregate, manage, and analyze over 1.12M images.