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

该项目的目标是开发方法，使研究人员能够远程和自动监测灵长类动物和其他高度社会化动物的行为。pi将通过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，并识别面部表情和声音。动作、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、进食和睡觉。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因素是观察到复杂的社会互动通常可以被认为是由一系列频繁发生的基本行为组成的，这些行为由相对简单和独特的手势组成。因此，对复杂社会互动进行建模的任务可以分为两种模式——跨越短时间的基本行为和跨越相对较长时间的随机序列。除了推动计算科学的发展，这种记录行为并以高数据率分析行为的新方法可能会引起行为生态学家、社会生物学家和研究灵长类动物和其他高度社会性动物的神经科学家的兴趣。有了这些新工具，科学家们可以研究和理解行为，例如，在规划濒危物种保护工作的背景下，为健康研究建立准确的动物模型，并支持动物园的畜牧业决策。该项目将提供一个广泛的、带注释的数据存储库和相关算法，并将资助研究生，他们将在项目的各个方面获得实践培训。

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.