Collaborative Research: CDI-Type I: Computational Models for the Automatic Recognition of Non-Human Primate Social Behaviors

合作研究：CDI-Type I：自动识别非人类灵长类动物社会行为的计算模型

• 批准号: 1027724
• 负责人: Deniz Erdogmus
• 金额: $ 15.58万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1027724&HistoricalAwards=false
• 关键词: Collaborative; Research; CDI; Type; Computational

The goal of this project is to develop methods that will permit researchers to remotely and automatically monitor behavior of primates and other highly social animals. The PIs will collect behavioral data from cameras and microphones. They will then develop statistical models and computational algorithms to track the individuals in the group and to recognize facial expressions and vocalizations. Patterns in movements, expressions, and vocalizations will be used to develop behavior-identifying algorithms that will recognize different behaviors such as aggression, submission, grooming, eating and sleeping. The project is a collaboration between computer scientists and primatologists. A key element of this project is the observation that complex social interactions can often be regarded as being composed of sequences of elementary behaviors which occur frequently and consist of relatively simple and distinct gestures. Thus, the task of modeling complex social interactions can be broken down into two regimes ? elementary behaviors spanning short duration, and their stochastic sequences spanning relatively longer time duration. Apart from advancing computational science, the new methods for recording behavior unobtrusively and analyzing them at a high data rate are likely to be of interest to behavioral ecologists, socio-biologists and neuroscientists in studies of primates and other highly social animals. With these new tools, scientists can study and understand behavior, for example, in the context of planning conservation efforts for threatened species, building accurate animal models for health research, and supporting animal husbandry decisions in zoos. The project will provide an extensive, annotated data repository and associated algorithms and will also fund graduate students who will gain hands-on training in all aspects of the project.

该项目的目标是开发方法，使研究人员能够远程自动监测灵长类动物和其他高度社会化动物的行为。PI将从摄像头和麦克风收集行为数据。然后，他们将开发统计模型和计算算法来跟踪群体中的个体，并识别面部表情和发声。运动、表情和发声的模式将用于开发行为识别算法，该算法将识别不同的行为，如攻击、服从、梳理、进食和睡眠。该项目是计算机科学家和灵长类动物学家之间的合作。这个项目的一个关键要素是观察到，复杂的社会互动通常可以被视为由一系列频繁发生的基本行为组成，并且由相对简单和独特的手势组成。因此，建模复杂的社会互动的任务可以分为两个制度？持续时间较短的基本行为及其持续时间相对较长的随机序列。除了推进计算科学之外，不引人注目地记录行为并以高数据率分析它们的新方法可能会引起行为生态学家，社会生物学家和神经科学家对灵长类动物和其他高度社会化动物研究的兴趣。有了这些新工具，科学家们可以研究和理解行为，例如，在规划受威胁物种的保护工作，为健康研究建立准确的动物模型，以及支持动物园的畜牧业决策的背景下。该项目将提供一个广泛的、带注释的数据储存库和相关算法，还将资助研究生，他们将获得该项目各个方面的实践培训。