Sbir phase I: automated processing of images and video for wildlife conservation

This $255,993 Project Grant from the National Science Foundation will fund Wildlife Imaging Systems LLC to develop automated video processing software and services for wildlife conservation. Through its Small Business Innovation Research program, NSF aims to foster innovation and support small businesses engaged in scientific and engineering R&D. Wildlife Imaging will use the grant to create a web-based software-as-a-service platform that can detect, track, and classify wildlife in field-captured video and images, reducing the complexity of obtaining quantitative data for biologists. The company will develop commercial off-the-shelf equipment packages and automated video analysis tools to make transformative wildlife monitoring technologies more accessible. The research focuses on algorithms to automate object detection and classification from video in a way that allows non-experts to easily build accurate machine learning models with minimal data labeling. This award will support the company's work from February 2022 through January 2023.

Abstract

The broader impact of this small business innovation research (SBIR) phase I project is to revolutionize wildlife field work and research. Many wildlife professionals already understand the potential for using video as a sensor, but without commercial software to turn video into data, its vast benefits remain untapped. IT is important to monitor environmental effects and the impact of urban growth; billions of dollars are spent annually understanding how construction and infrastructure projects are impacting species and habitats. The state of practice is to send technicians into the field for observation. In many situations using a camera system to monitoring for wildlife is not only safer and more effective, but also much less expensive that using human labor. This project will reduce the cost, decrease the timeline, and yield better outcomes for these important conservation efforts, allowing the existing biodiversity to be preserved. This small business innovation research (SBIR) phase I project will develop a web-based software-as-a-service to detect, track, and classify wildlife from video and images captured in the field. There is substantial potential for video to provide better data to help wildlife biologists perform their work, but the main barrier is the complexity. Using video systems to study wildlife requires advanced multidisciplinary knowledge; appropriate camera technology, recording methods, advanced video processing techniques and machine learning algorithms. There are currently no commercial-off-the-shelf hardware or software packages available for biologists. This project will reduce the complexity of obtaining quantitative data from video by developing commercial-off-the-shelf equipment packages, automated video processing software, and the advisory services needed to make this transformational technology more accessible to biologists in the field. The video processing needed is the focus of the research; the goal is the create algorithms that automate object detection and classification from the video and allow non-experts to create an accurate machine learning classifier with minimal labeling requirements. 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.