Beyond frames: A contextual deep learning approach for classroom action understanding

This paper proposes a Contextual Action Recognition System for classroom monitoring that integrates spatial and temporal modeling with contextual analysis for enhanced robustness and interpretability. YOLO (You Only Look Once) is used for spatial feature extraction, while ConvLSTM and Temporal Convolutional Network (TCN) capture temporal patterns. An attention-based fusion mechanism adaptively weights their outputs. A hybrid proximity detection approach, combining overlap assessment with Intersection over Union (IoU) thresholding outperforms traditional IoU-based methods, especially in perspectivedistorted scenes. The model achieved strong results: precision of 0.90, recall of 0.87, F 1 -score of 0.86, and $\mathrm{mAP} \text{@} 0.5$ of 0.92. It outperformed baselines like SlowFast and ACRN on datasets including AVA, UCF101-24, and JHMDB-21, demonstrating superior spatial relationship detection.