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.

翻译：视频活动检测研究主要关注于在短视频片段中识别定义明确的人类活动。大多数视频活动识别研究集中于开发需要在大规模视频数据集上进行训练的大参数系统。本文开发了一种低参数、模块化系统，具备快速推理能力，可在有限数据集上完全训练，而无需从大参数系统进行迁移学习。该系统能够准确检测真实课堂视频中特定活动，并将这些活动与执行活动的学生关联起来。此外，本文还开发了一个基于网络的交互式应用程序，用于在真实课堂长视频上可视化人类活动地图。