Student activity recognition from classroom video: a survey

Abstract

Student behavior and activity play a crucial role in shaping the classroom atmo sphere and influencing the quality of a learning session. Recently, vision-based student activity recognition has gained significant attention. However, recog nizing student activities from classroom videos presents unique challenges due to the nature of the classroom environment, such as the presence of multiple students and severe occlusions. As a result, research in this area has often over looked these challenges. This study provides a detailed and comprehensive re view of student activity recognition from classroom videos. First, we formalize the problem of student activity recognition from videos and categorize existing methods into three distinct approaches: frame-level, clip-level, and continuous recognition. We then provide a detailed analysis of representative methods for each approach. In addition, we present a comprehensive overview of publicly available datasets for student activity recognition and discuss key open chal lenges, together with potential future research directions. Our analysis reveals that: (1) Most existing studies focus on frame-level recognition, while clip-based and continuous activity recognition remain relatively underexplored; (2) there is still a lack of large-scale, standardized benchmark datasets for vision-based stu dent activity recognition; and (3) existing research primarily emphasizes recog nition accuracy, whereas real-time performance and computational efficiency are rarely addressed.