Classroom Behavior Recognition: A Spatiotemporal Frequency-Domain Approach with Imbalanced Data Handling

Existing video action recognition methods face two challenges when applied to classroom surveillance: first, they struggle to balance local spatial and global temporal information; second, they are affected by imbalanced category distribution where routine behaviors dominate while educationally significant rare behaviors are often missed. To address these issues, this paper proposes two components. A Regional Global-Time Featurizer (RGTF) extracts compact local spatial and global temporal features via frequency-domain transformation with low computational overhead. An Exponential Focal Loss (EFL) adaptively reweights hard samples to mitigate the impact of imbalanced data. Experiments on XDCR, AVA2.2, Kinetics-400, UCF101, and Something-Something V2 show that RGTF improves baseline models by up to 1.8% and EFL outperforms standard losses by up to 0.92%. An optional LLM-assisted module is additionally provided as an application example to illustrate one possible way of using the recognition outputs for generating qualitative classroom feedback. This module is independent of the core recognition pipeline.