CRII: OAC: Data Collection Infrastructure for Panoramic Video Monitoring in Wildlife Science

Wildlife monitoring has significant scientific and societal impacts. By utilizing remote cameras, biologists and ecologists can monitor and manage wildlife in order to prevent the transmission of zoonotic disease from animals and the invasion of wildlife on crops and livestock. However, current cyberinfrastructure (CI) in wildlife monitoring is limited to normal angle videos with a limited field of view and has caused missing the recording of important events that occurred outside of the direction being filmed.

Moreover, existing remote cameras only allow the recording of short videos for a few minutes and thus cannot document many hours of wildlife activity in the monitoring zone. This project proposes methods for panoramic video monitoring that capture 360 degree uninterrupted videos to document complete wildlife activities. The project will allow wildlife scientists to access high fidelity monitoring data in both the spatial and temporal domains.

Panoramic videos will not only capture comprehensive details on and near the monitoring site, but also depict the monitoring context of the data collection. The abundant research data and metadata embedded in panoramic videos will enhance the productivity of biologists and ecologists. If successful, the proposed wildlife monitoring CI will accelerate the adoption of panoramic data collection in other field research such as agriculture and archeology.

The research outcomes, including the datasets generated and the software developed, will provide an interdisciplinary opportunity for undergraduate research, course curriculum development, and high school outreach activities, especially for underrepresented groups.

This project investigates a video collection cyberinfrastructure to enable panoramic wildlife monitoring. The design objective is to archive days to weeks of high resolution video data for long lived monitoring under the limited storage and energy constraints of remote cameras. To this end, this project proposes a framework for collaborative local and networked storage.

First, we propose camera computing strategies to understand the scientific value of monitoring content and maximally compress the video with negligible overhead. This would mitigate the overall need for storage. Second, we propose a networked storage scheme to address the intermittent nature of the network in the wild, where only partial video is transported while the remaining video is generated in the receiver.

We then schedule compressed video tiles for local storage or networked storage by orchestrating the storage, network and battery resources. Finally, we will develop and deploy the panoramic video monitoring in real wildlife research. We will validate the CI on the Savannah River site and assist wildlife scientists to study the impacts of animal interaction on disease transmission.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.