Low-Light Gaze Estimation for Fine-grained Intelligent Classroom Behavior Monitoring

Computer Vision CV technology is crucial for intelligent classroom behavior monitoring. Current CV methods can only measure coarse-grained metrics like attendance rate and head-up rate, while gaze estimation enables fine-grained monitoring of each student. However, existing gaze estimation algorithms struggle in low-light classroom environments. To address this, we propose the LLSGE-Net framework, which integrates low-light image enhancement with gaze estimation. This multi-stage enhancement and calibration process significantly improves image quality. Our method utilizes Local-Global Context Fusion ALGCF for better eye and face feature integration, and a feature enhancement technique combining 1D convolution and group normalization. The Enhanced Local Spatial and Global Channel Attention ELSCA improves the localization of regions of interest, while the Deep Feature Extraction Network DFENet refines high-level features. Extensive experiments demonstrate the superiority of our approach in real-world low-light classroom scenarios for student attention detection and behavior monitoring.