Research on video activity detection has primarily focused on identifying
well-defined human activities in short video segments. The majority of the
research on video activity recognition is focused on the development of large
parameter systems that require training on large video datasets. This paper
develops a low-parameter, modular system with rapid inferencing capabilities
that can be trained entirely on limited datasets without requiring transfer
learning from large-parameter systems. The system can accurately detect and
associate specific activities with the students who perform the activities in
real-life classroom videos. Additionally, the paper develops an interactive
web-based application to visualize human activity maps over long real-life
classroom videos.

本研究开发了一种低参数、模块化系统，具有快速推理能力，在有限数据集上完全可以进行训练，无需从大参数系统进行迁移学习。该系统能够准确检测并关联实际课堂视频中执行这些活动的学生，并开发了一个基于交互式 Web 应用程序来可视化长时间真实课堂视频中的人类活动地图。