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使用f-formation检测社会交互群体：分类法、方法、数据集、应用、挑战及未来研究方向的综述

Detecting socially interacting groups using f-formation: A survey of taxonomy, methods, datasets, applications, challenges, and future research directions

TCS Research（塔塔咨询服务研究中心）；Myanmar Institute of Information Technology（缅甸信息技术学院）

Hrishav Bakul Barua, Theint Haythi Mg, Pradip Pramanick, Chayan Sarkar

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Preprint submitted for Review
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AI总结：

本文综述了基于f-formation的社会交互群体检测研究，提出了一个整体综述框架，涵盖分类法、方法、数据集、应用、挑战及未来方向。

摘要：

我们日常环境中的机器人日益增多。它们的可用性和可接受性在很大程度上取决于它们与人类同伴进行显式和隐式交互的能力。因此，社会行为是机器人最令人期待的品质之一。然而，并没有特定的方面和/或特征来定义社会可接受的行为，这在很大程度上取决于情境、应用和社会环境。在本文中，我们研究了共处机器人的一种此类社会行为。想象一群人正在互相交流，而我们想加入这个群体。作为人类，我们以社会可接受的方式做到这一点，即在群体内，我们将自己定位在可以参与群体活动而不打扰/阻碍任何人的位置。为了具备这种品质，机器人首先需要确定群体的阵型，然后为自己确定一个位置，这是我们人类隐式完成的。f-formation理论可用于此目的。由于阵型类型可能非常多样，检测社会群体并非一项简单的任务。在本文中，我们对使用f-formation进行社会交互和群体检测的现有工作进行了全面综述，涵盖机器人学及其他应用。我们还提出了一个新颖的整体综述框架，结合了与该问题相关的所有可能关注点和模块。我们基于方法、摄像机视角、数据集、检测能力和规模、评估方法以及应用领域定义了分类法。我们讨论了当前文献中的某些开放挑战和局限性，以及基于该框架的未来研究方向。特别地，我们讨论了现有的方法/技术及其相对优缺点、应用，并提供了一系列该领域尚未解决但相关的问题。

英文摘要：

Robots in our daily surroundings are increasing day by day. Their usability and acceptability largely depend on their explicit and implicit interaction capability with fellow human beings. As a result, social behavior is one of the most sought-after qualities that a robot can possess. However, there is no specific aspect and/or feature that defines socially acceptable behavior and it largely depends on the situation, application, and society. In this article, we investigate one such social behavior for collocated robots. Imagine a group of people is interacting with each other and we want to join the group. We as human beings do it in a socially acceptable manner, i.e., within the group, we do position ourselves in such a way that we can participate in the group activity without disturbing/obstructing anybody. To possess such a quality, first, a robot needs to determine the formation of the group and then determine a position for itself, which we humans do implicitly. The theory of f-formation can be utilized for this purpose. As the types of formations can be very diverse, detecting the social groups is not a trivial task. In this article, we provide a comprehensive survey of the existing work on social interaction and group detection using f-formation for robotics and other applications. We also put forward a novel holistic survey framework combining all the possible concerns and modules relevant to this problem. We define taxonomies based on methods, camera views, datasets, detection capabilities and scale, evaluation approaches, and application areas. We discuss certain open challenges and limitations in current literature along with possible future research directions based on this framework. In particular, we discuss the existing methods/techniques and their relative merits and demerits, applications, and provide a set of unsolved but relevant problems in this domain.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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