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<p>Classroom management is a crucial component in ensuring teaching quality. However, traditional methods that rely primarily on manual processes cannot provide continuous assessments in an unobtrusive manner. Recently, previous studies that have explored the use of visual data to support classroom management have yielded promising results. However, the majority focus on recognizing activities at the frame-level or clip-level, which limits their practical application. In this paper, we present a novel method for continuous student activity recognition. We formulate continuous activity recognition as a multiple object tracking (MOT) problem, where each activity instance is treated as a moving object that appears in different regions of the frames over time. The proposed method named STrack4Re (<b>S</b>tudent Activity <b>Track</b> to <b>Re</b>congize) consists of two steps: a detection step and a tracking step. The main goal of the detection step is to identify the region in the frame where the activity occurs. Once an activity instance is detected, a tracker is initialized to follow this instance over time. In our study, YOLOv5 and OC-SORT have been chosen for object detection and tracking. YOLOv5 has proven effective for static student activity detection, while OC-SORT is a tracking algorithm known for its robustness to occlusions. Therefore, the proposed method could handle occlusion issue in classroom videos. To evaluate the proposed method, we created a new dataset named CStudentAct, which comprises video sequences with appropriate annotations for continuous student activity recognition.</p>

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A method for continuous student activity recognition from classroom videos

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • Phuong-Dung Nguyen,
  • Ngoc-Trang Le,
  • Khanh-Huyen Bui,
  • Hong-Quan Nguyen,
  • Huu-Quynh Nguyen,
  • Thi-Lan Le

摘要

Classroom management is a crucial component in ensuring teaching quality. However, traditional methods that rely primarily on manual processes cannot provide continuous assessments in an unobtrusive manner. Recently, previous studies that have explored the use of visual data to support classroom management have yielded promising results. However, the majority focus on recognizing activities at the frame-level or clip-level, which limits their practical application. In this paper, we present a novel method for continuous student activity recognition. We formulate continuous activity recognition as a multiple object tracking (MOT) problem, where each activity instance is treated as a moving object that appears in different regions of the frames over time. The proposed method named STrack4Re (Student Activity Track to Recongize) consists of two steps: a detection step and a tracking step. The main goal of the detection step is to identify the region in the frame where the activity occurs. Once an activity instance is detected, a tracker is initialized to follow this instance over time. In our study, YOLOv5 and OC-SORT have been chosen for object detection and tracking. YOLOv5 has proven effective for static student activity detection, while OC-SORT is a tracking algorithm known for its robustness to occlusions. Therefore, the proposed method could handle occlusion issue in classroom videos. To evaluate the proposed method, we created a new dataset named CStudentAct, which comprises video sequences with appropriate annotations for continuous student activity recognition.