The state-of-the art solutions for human activity understanding from a video
stream formulate the task as a spatio-temporal problem which requires joint
localization of all individuals in the scene and classification of their
actions or group activity over time. Who is interacting with whom, e.g. not
everyone in a queue is interacting with each other, is often not predicted.
There are scenarios where people are best to be split into sub-groups, which we
call social groups, and each social group may be engaged in a different social
activity. In this paper, we solve the problem of simultaneously grouping people
by their social interactions, predicting their individual actions and the
social activity of each social group, which we call the social task. Our main
contributions are: i) we propose an end-to-end trainable framework for the
social task; ii) our proposed method also sets the state-of-the-art results on
two widely adopted benchmarks for the traditional group activity recognition
task (assuming individuals of the scene form a single group and predicting a
single group activity label for the scene); iii) we introduce new annotations
on an existing group activity dataset, re-purposing it for the social task.

本文提出了一个端到端的可训练框架来解决人类活动理解中的社交任务。该框架同时分组人们，预测他们的个人行为和每个社交团体的社交活动，并提供了新的数据集注释。