Self-supervised Social Relation Representation for Human Group Detection
This work addresses video-based human social activity analysis by improving group detection, but it appears incremental as it builds on existing methods with a novel framework.
The paper tackles human group detection in crowds by proposing a two-stage multi-head framework that uses self-supervised social relation embedding, achieving remarkable performance on large-scale benchmarks like PANDA and JRDB-Group with very few labeled training data.
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.