SBIR Phase I: Automated processing of images and video for wildlife conservation

这项小企业创新研究（SBIR）第一阶段项目的广泛影响是彻底改变野生动物实地工作和研究。许多野生动物专业人士已经意识到使用视频作为传感器的潜力，但没有商业软件将视频转换为数据，其巨大的好处仍未得到开发。必须监测环境影响和城市增长的影响；每年花费数十亿美元来了解建筑和基础设施项目如何影响物种和栖息地。目前的做法是派技术人员到实地观察。在许多情况下，使用摄像系统监测野生动物不仅更安全、更有效，而且比使用人工成本低得多。该项目将降低成本，缩短时间，并为这些重要的保护工作带来更好的结果，使现有的生物多样性得到保护。这项小企业创新研究（SBIR）第一阶段项目将开发一种基于网络的软件即服务，用于从现场拍摄的视频和图像中检测、跟踪和分类野生动物。视频有很大的潜力可以提供更好的数据来帮助野生动物生物学家完成他们的工作，但主要的障碍是复杂性。使用视频系统研究野生动物需要先进的多学科知识；适当的摄像技术，记录方法，先进的视频处理技术和机器学习算法。目前还没有现成的商用硬件或软件包可供生物学家使用。该项目将通过开发商业化的现成设备包、自动视频处理软件和咨询服务，减少从视频中获取定量数据的复杂性，从而使该领域的生物学家更容易获得这一转化技术。所需的视频处理是研究的重点；目标是创建算法，自动从视频中检测和分类对象，并允许非专家以最小的标签要求创建准确的机器学习分类器。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.