Self-supervised Social Relation Representation for Human Group Detection

• URL: http://arxiv.org/abs/2203.03843v1
• Date: Tue, 8 Mar 2022 04:26:07 GMT
• Title: Self-supervised Social Relation Representation for Human Group Detection
• Authors: Jiacheng Li, Ruize Han, Haomin Yan, Zekun Qian, Wei Feng, Song Wang
• Abstract summary: 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. In the second stage, based on the social relation embedding, we develop a self-attention inspired network for human group detection.
• Score: 18.38523753680367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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