EgoGroups: A Benchmark For Detecting Social Groups of People in the Wild
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910066240651264 |
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| author | Murrugarra-Llerena, Jeffri Chitale, Pranav Liu, Zicheng Ao, Kai Ham, Yujin Balakrishnan, Guha Cascante-Bonilla, Paola |
| author_facet | Murrugarra-Llerena, Jeffri Chitale, Pranav Liu, Zicheng Ao, Kai Ham, Yujin Balakrishnan, Guha Cascante-Bonilla, Paola |
| contents | Social group detection, or the identification of humans involved in reciprocal interpersonal interactions (e.g., family members, friends, and customers and merchants), is a crucial component of social intelligence needed for agents transacting in the world. The few existing benchmarks for social group detection are limited by low scene diversity and reliance on third-person camera sources (e.g., surveillance footage). Consequently, these benchmarks generally lack real-world evaluation on how groups form and evolve in diverse cultural contexts and unconstrained settings. To address this gap, we introduce EgoGroups, a first-person view dataset that captures social dynamics in cities around the world. EgoGroups spans 65 countries covering low, medium, and high-crowd settings under four weather/time-of-day conditions. We include dense human annotations for person and social groups, along with rich geographic and scene metadata. Using this dataset, we performed an extensive evaluation of state-of-the-art VLM/LLMs and supervised models on their group detection capabilities. We found several interesting findings, including VLMs and LLMs can outperform supervised baselines in a zero-shot setting, while crowd density and cultural regions clearly influence model performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22249 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EgoGroups: A Benchmark For Detecting Social Groups of People in the Wild Murrugarra-Llerena, Jeffri Chitale, Pranav Liu, Zicheng Ao, Kai Ham, Yujin Balakrishnan, Guha Cascante-Bonilla, Paola Computer Vision and Pattern Recognition Social group detection, or the identification of humans involved in reciprocal interpersonal interactions (e.g., family members, friends, and customers and merchants), is a crucial component of social intelligence needed for agents transacting in the world. The few existing benchmarks for social group detection are limited by low scene diversity and reliance on third-person camera sources (e.g., surveillance footage). Consequently, these benchmarks generally lack real-world evaluation on how groups form and evolve in diverse cultural contexts and unconstrained settings. To address this gap, we introduce EgoGroups, a first-person view dataset that captures social dynamics in cities around the world. EgoGroups spans 65 countries covering low, medium, and high-crowd settings under four weather/time-of-day conditions. We include dense human annotations for person and social groups, along with rich geographic and scene metadata. Using this dataset, we performed an extensive evaluation of state-of-the-art VLM/LLMs and supervised models on their group detection capabilities. We found several interesting findings, including VLMs and LLMs can outperform supervised baselines in a zero-shot setting, while crowd density and cultural regions clearly influence model performance. |
| title | EgoGroups: A Benchmark For Detecting Social Groups of People in the Wild |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.22249 |