  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.
