Dense Student Behavior Recognition Algorithm for Classroom Surveillance Based on Improved Real-Time Detection Transformer

Accurate detection of students' behaviors in class-rooms through video surveillance is crucial for improving teaching quality. This paper presents LAF -DETR, a novel algorithm designed to address challenges such as dense student interactions and multi-scale variations in complex classroom environments. Key contributions include a lightweight convolution module to enhance the extraction of multi-scale features, a feature fusion module to reduce background noise and improve multi-scale feature integration, and a downsampling module to capture compre-hensive information across various behavior scales. Experiments on three public datasets (SCB-Dataset3-S, CrowdHuman, Smart-Classroom-Student-Behavior) show that LAF-DETR outperforms the baseline model by 2.8%, 1.9%, and 2.1 % in Average Precision (AP), respectively, demonstrating its effectiveness in handling occlusion and multi-scale detection challenges.