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

密苏里大学和伊利诺伊大学厄巴纳-香槟分校获得合作拨款，以开发先进的计算和信息学工具，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1)先进的计算机视觉方法，用于在动态和杂乱的环境中检测和跟踪动物；2)自适应分类、机器学习和信息融合方法，用于识别动物物种和个体ID；以及3)数据摘要和数据库管理方案，以支持合作的野生动物研究。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实地研究进行评估，以确定它们支持合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、生态学和保护研究的最先进水平。它将为大规模的海量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。这将使野生动物研究人员的个人或协调网络能够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对野生动物进行协作监测和跟踪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。数据库将由Smithsonian托管。这将为指导研究生提供令人兴奋的跨学科机会，并让K-12和本科生参与到专业指导的研究中。该项目的软件和结果将从网站http://videonet.ece.missouri.edu.上获得

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.