Enhancing Continuous Student Activity Recognition through Virtual Trajectory and Appearance Matching

Classroom activities play a vital role in assessing teaching quality. While numerous studies have investigated activity recognition using classroom visual data, this task remains challenging due to crowded scenes and frequent occlusions. To overcome these challenges, we propose a two-step method for continuous student activity recognition: activity location detection and temporal tracking. For activity detection, we fine-tune two state-of-the-art object detectors to identify relevant regions in each frame. For tracking, we enhance the OC-SORT algorithm by incorporating bounding boxes generated from virtual trajectories and integrating appearance features to reduce tracking loss caused by occlusion. Our proposed method demonstrates robust and effective performance on the CStudentAct dataset, achieving superior tracking accuracy with MOTA improvements ranging from 0.1% to 2.6% across various activities, while also consistently attaining the highest F1-scores in activity recognition—outperforming the baseline by up to 10.9%.