构建首个面向真实教室监控图像的多模态活动识别数据集
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images
- 基于真实教室监控图像，涵盖32类活动与多视角数据
- 支持持续学习和小样本持续学习任务设置
- 适合研究教育场景下鲁棒活动识别的学者使用
AI+教育领域中的活动识别应用日益受到关注。然而，现有工作主要聚焦于人工采集视频中的活动识别，且活动类别有限，对真实教室监控图像中的活动识别关注较少。教室监控图像的活动识别面临类别不平衡、活动相似度高等挑战。为此，我们构建了一个新型多模态数据集ARIC（Activity Recognition In Classroom），专注于教室监控图像的活动识别。ARIC具有多视角、32个活动类别、三种模态及真实教室场景等优势。除常规活动识别任务外，还提供了持续学习与小样本持续学习设置。我们希望ARIC能推动开放教学场景下未来研究的发展。可从https://ivipclab.github.io/publication_ARIC/ARIC下载初步数据。
原文摘要 · Abstract (English)
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 from https://ivipclab.github.io/publication_ARIC/ARIC.
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