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-supervised 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 have released the source code to the public.
J. Li and R. Han—Equal contribution.
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Notes
- 1.
If a subject is missing in a frame, we fill it with blank (all-zero feature vector).
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grants U1803264, 62072334, and the Tianjin Research Innovation Project for Postgraduate Students under Grant 2021YJSB174.
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Li, J., Han, R., Yan, H., Qian, Z., Feng, W., Wang, S. (2022). Self-supervised Social Relation Representation for Human Group Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13695. Springer, Cham. https://doi.org/10.1007/978-3-031-19833-5_9
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