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Robots in our daily surroundings are increasing day by day. Their usabilityand acceptability largely depend on their explicit and implicit interactioncapability with fellow human beings. As a result, social behavior is one of themost sought-after qualities that a robot can possess. However, there is nospecific aspect and/or feature that defines socially acceptable behavior and itlargely depends on the situation, application, and society. In this article, weinvestigate one such social behavior for collocated robots. Imagine a group ofpeople is interacting with each other and we want to join the group. We ashuman beings do it in a socially acceptable manner, i.e., within the group, wedo position ourselves in such a way that we can participate in the groupactivity without disturbing/obstructing anybody. To possess such a quality,first, a robot needs to determine the formation of the group and then determinea position for itself, which we humans do implicitly. The theory of f-formationcan be utilized for this purpose. As the types of formations can be verydiverse, detecting the social groups is not a trivial task. In this article, weprovide a comprehensive survey of the existing work on social interaction andgroup detection using f-formation for robotics and other applications. We alsoput forward a novel holistic survey framework combining all the possibleconcerns and modules relevant to this problem. We define taxonomies based onmethods, camera views, datasets, detection capabilities and scale, evaluationapproaches, and application areas. We discuss certain open challenges andlimitations in current literature along with possible future researchdirections based on this framework. In particular, we discuss the existingmethods/techniques and their relative merits and demerits, applications, andprovide a set of unsolved but relevant problems in this domain.

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