Tracking Individuals in Classroom Videos via Post-processing OpenPose Data
Paul Hur, Nigel Bosch
Abstract
Analyzing classroom video data provides valuable insights about the interactions between students and teachers, albeit often through time-consuming qualitative coding or the use of bespoke sensors to record individual movement information. We explore measuring classroom posture and movement in secondary classroom video data through computer vision methods (especially OpenPose), and introduce a simple but effective approach to automatically track movement via post-processing of OpenPose output data. Analysis of 67 videos of mathematics classes from middle school and high school levels highlighted the challenges associated with analyzing movement in typical classroom videos: occlusion from low camera angles, difficulty detecting lower body movement due to sitting, and the close proximity of students to one another and their teachers. Despite these challenges, our approach tracked person IDs across classroom videos for 93.0% of detected individuals. The tracking results were manually verified through randomly sampling 240 instances, which revealed notable OpenPose tracking inconsistencies. Finally, we discuss the implications for supporting more scalability of video data classroom movement analysis, and future potential explorations.