Collaborative Research: ABI Innovation : Computational and Informatics Tools for Supporting Collaborative Wildlife Monitoring and Research

协作研究：ABI 创新：支持协作野生动物监测和研究的计算和​​信息学工具

• 批准号: 1062351
• 负责人: Thomas Huang
• 金额: $ 44.34万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1062351&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Computational

The University of Missouri and the University of Illinois at Urbana-Champaign are awarded collaborative grants to develop advanced computational and informatics tools that will support wildlife data collection, analysis, and management at large scales. Project objectives include investigation of 1) advanced computer vision methods for detecting and tracking animals in dynamic and cluttered environments; 2) adaptive classification, machine learning, and information fusion methods for recognizing animal species and individual ID; and 3) data summarization and database management schemes to support collaborative wildlife research. The performance of these computational and informatics tools will be evaluated using existing camera trap datasets and field studies in terms of their potential to support collaborative wildlife research. This project will broadly advance the state-of-the-art in computer vision, wildlife monitoring, ecology, and conservation research. It will provide new methods and tools for automated processing and mining of massive wildlife monitoring data at large scales. This will allow individual or coordinated networks of wildlife researchers to analyze and manage camera-trap data with minimum effort and compare and share data between research groups across different geographical regions. Collaborative wildlife monitoring and tracking at large geographical and time scales will help us understand the complex dynamics of wildlife systems, evaluate the impact of human actions and environmental changes on wildlife species, and answer many important wildlife, ecological, and conservation research questions. The database will be hosted by Smithsonian. This will provide exciting interdisciplinary opportunities for mentoring graduate students and involving K-12 and undergraduate students into professionally guided research. Software and results of this project will be available from the website http://videonet.ece.missouri.edu.

密苏里大学和伊利诺伊大学厄巴纳-香槟分校获得合作资助，开发先进的计算和信息学工具，以支持大规模的野生动物数据收集、分析和管理。项目目标包括研究1)用于在动态和杂乱环境中检测和跟踪动物的先进计算机视觉方法；2)自适应分类、机器学习和信息融合方法识别动物物种和个体ID；3)数据汇总和数据库管理方案，支持野生动物协同研究。这些计算和信息学工具的性能将使用现有的相机陷阱数据集和实地研究来评估它们支持协作野生动物研究的潜力。该项目将广泛推进计算机视觉、野生动物监测、生态学和保护研究方面的最新技术。它将为大规模野生动物监测数据的自动化处理和挖掘提供新的方法和工具。这将允许野生动物研究人员的个人或协调网络以最小的努力分析和管理相机陷阱数据，并在不同地理区域的研究小组之间比较和共享数据。在大的地理和时间尺度上协作监测和跟踪野生动物将有助于我们了解野生动物系统的复杂动态，评估人类活动和环境变化对野生动物物种的影响，并回答许多重要的野生动物、生态和保护研究问题。该数据库将由史密森学会托管。这将为指导研究生提供令人兴奋的跨学科机会，并使K-12和本科生参与专业指导的研究。该项目的软件和结果可从网站http://videonet.ece.missouri.edu获得。