ARIC: An Activity Recognition Dataset in Classroom Surveillance Images

TL;DR

This paper introduces ARIC, a comprehensive multimodal dataset of classroom surveillance images with 32 activity categories, addressing challenges like class imbalance and activity similarity to advance activity recognition in educational settings.

Contribution

The paper presents a novel, real-world classroom dataset with multiple perspectives and modalities, supporting general, continual, and few-shot learning tasks for activity recognition.

Findings

ARIC dataset includes 32 activity categories in real classroom scenarios.

Supports continual learning and few-shot learning experiments.

Facilitates future research in AI-based classroom activity analysis.

Abstract

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.