ARIC: An Activity Recognition Dataset in Classroom Surveillance Images

The application of activity recognition in the ``AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in manually captured videos and a limited number of activity types, with little attention given to recognizing activities in surveillance images from real classrooms. Activity recognition in classroom surveillance images faces multiple challenges, such as class imbalance and high activity similarity. To address this gap, we constructed a novel multimodal dataset focused on classroom surveillance image activity recognition called ARIC (Activity Recognition In Classroom). The ARIC dataset has advantages of multiple perspectives, 32 activity categories, three modalities, and real-world classroom scenarios. In addition to the general activity recognition tasks, we also provide settings for continual learning and few-shot continual learning. We hope that the ARIC dataset can act as a facilitator for future analysis and research for open teaching scenarios. You can download preliminary data fromthis https URL.

@article{xu2025_2410.12337,
  title={ ARIC: An Activity Recognition Dataset in Classroom Surveillance Images },
  author={ Linfeng Xu and Fanman Meng and Qingbo Wu and Lili Pan and Heqian Qiu and Lanxiao Wang and Kailong Chen and Kanglei Geng and Yilei Qian and Haojie Wang and Shuchang Zhou and Shimou Ling and Zejia Liu and Nanlin Chen and Yingjie Xu and Shaoxu Cheng and Bowen Tan and Ziyong Xu and Hongliang Li },
  journal={arXiv preprint arXiv:2410.12337},
  year={ 2025 }
}