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Urbana-Champaign大学获得了合作赠款，以开发高级计算和信息学工具，以大规模支持野生动植物数据收集，分析和管理。 项目目标包括对1）用于在动态和混乱环境中检测和跟踪动物的高级计算机视觉方法的调查； 2）用于识别动物物种和单个ID的自适应分类，机器学习和信息融合方法； 3）数据摘要和数据库管理方案，以支持协作野生动植物研究。 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现场研究来评估，以支持协作野生动植物研究的潜力。 该项目将广泛推进计算机视觉，野生动植物监测，生态和保护研究的最先进。 它将为大规模的大规模野生动植物监测数据提供新的方法和工具。 这将使野生动植物研究人员的个人或协调网络以最少的精力分析和管理相机陷阱数据，并比较不同地理区域的研究小组之间的数据。 大型地理和时间尺度上的协作野生动植物监测和跟踪将有助于我们了解野生动植物系统的复杂动态，评估人类行为和环境变化对野生动植物物种的影响，并回答许多重要的野生动植物，生态和保护研究问题。 该数据库将由史密森尼（Smithsonian）托管。 这将为指导研究生提供激动人心的跨学科机会，并让K-12和本科生参与专业的指导研究。 该项目的软件和结果将从网站http://videonet.ece.missouri.edu获得。