  Human group detection, which splits crowd of people into groups, is an
important step for video-based human social activity analysis. The core of
human group detection is the human social relation representation and
division.In this paper, we propose a new two-stage multi-head framework for
human group detection. In the first stage, we propose a human behavior
simulator head to learn the social relation feature embedding, which is
self-supervisely trained by leveraging the socially grounded multi-person
behavior relationship. In the second stage, based on the social relation
embedding, we develop a self-attention inspired network for human group
detection. Remarkable performance on two state-of-the-art large-scale
benchmarks, i.e., PANDA and JRDB-Group, verifies the effectiveness of the
proposed framework. Benefiting from the self-supervised social relation
embedding, our method can provide promising results with very few (labeled)
training data. We will release the source code to the public.
