  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.
