TY - GEN
T1 - Tracking Individuals in Classroom Videos via Post-processing OpenPose Data
AU - Hur, Paul
AU - Bosch, Nigel
N1 - This material is based upon work supported by the National Science Foundation (DRL-1920796). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
PY - 2022/3/21
Y1 - 2022/3/21
N2 - 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.
AB - 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.
KW - classroom video
KW - movement
KW - posture
KW - video analysis
UR - http://www.scopus.com/inward/record.url?scp=85126176876&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126176876&partnerID=8YFLogxK
U2 - 10.1145/3506860.3506888
DO - 10.1145/3506860.3506888
M3 - Conference contribution
AN - SCOPUS:85126176876
T3 - ACM International Conference Proceeding Series
SP - 465
EP - 471
BT - LAK 2022 - Conference Proceedings
PB - Association for Computing Machinery
T2 - 12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Y2 - 21 March 2022 through 25 March 2022
ER -