EAGER: Modeling and Recognizing Collective Activities

这个项目探索了一个新的原则性框架，用于学习集体活动的一般模式。集体活动的例子有：人们交谈；一群斑马逃离狮子。这样的模型又用于检测、分类和分割活动，以及从视频序列中识别不同于集体行为的活动。本项目中开展的研究与以前的行动分类研究有明显不同，在行动分类研究中，活动是通过孤立地考虑个人来分析的。此外，与当前的许多贡献不同，该项目的目标是在动态杂乱的背景、运动、单目和未校准的摄像机等不受限制的条件下工作。该项目的关键智力贡献是：i)基于随机森林的学习方案，该方案能够自适应地表征个体的连贯行为，从而能够对集体活动进行区分分类。该学习方案还与使用上下文的其他视觉识别任务相关(例如，场景和对象识别)；ii)基于关系依赖网络的方法，用于分割不同的集体活动并发现异常活动。该项目可以为解决高级视觉问题提供关键的构建块，例如对人/动物和对象之间的交互进行建模，构建人/动物活动的本体，对复杂的人/动物行为进行建模。这项研究有可能在机器人和导航等战略领域发挥变革性的作用。它还为分析和研究生物(昆虫、动物)或生物医学(细胞)中典型的时空集体行为提供了重要工具。

This project explores a novel principled framework for learning generic models of collective activities. Examples of collective activities are: people talking; a group of zebras escaping from a lion. Such models are used, in turn, for detecting, classifying, and segmenting activities as well as indentifying activities that differ from the collective behavior from videos sequences. Research developed in this project is distinctly different from previous research on action classification wherein activities are analyzed by considering individuals in isolation. Furthermore, unlike many current contributions, the aim is to work under unrestrictive conditions such as dynamic cluttered background, moving, monocular and un-calibrated cameras.Key intellectual contributions of this project are: i) a learning scheme based on Random Forest that is able to adaptively characterize the coherent behavior of individuals, thus enabling discriminative classification of collective activities. This learning scheme is also relevant to other visual recognition tasks using context (e.g., scene and object recognition); ii) a methodology based on Relational Dependency Networks for segmenting different collective activities and discovering anomalous ones. This project can provides critical building blocks toward addressing high level visual problems such as modeling the interaction between humans/animals and objects, constructing an ontology of human/animal activities, modeling complex human/animal behaviors. This research has a potential to play a transformative role in strategic areas such as robotics and navigation. It also provides a crucial tool for analyzing and studying typical spatial-temporal collective behaviors in biology (insects, animals) or biomedicine (cells).