论文标题
学生在学校的危险行为检测
Student Dangerous Behavior Detection in School
论文作者
论文摘要
已经安装了视频监视系统，以确保学校的学生安全。但是，发现危险的行为，例如战斗和摔倒，通常取决于人类的观察。在本文中，我们专注于自动检测学生的危险行为，这面临许多挑战，例如数据集不足，令人困惑的姿势，关键框架检测和及时响应。为了应对这些挑战，我们首先使用监视视频的位置和标签构建了危险行为数据集，并将长视频的动作识别转换为避免键框检测的对象检测任务。然后，我们提出了一种名为DangerDet的新型端到端危险行为检测方法，该方法结合了多尺度的身体特征和基于关键的姿势特征。由于姿势和行为之间的高度相关性，我们可以提高行为分类的准确性。在我们的数据集中，DangerDet以约11 fps的形式实现71.0 \％地图。它可以在准确性和时间成本之间保持更好的平衡。
Video surveillance systems have been installed to ensure the student safety in schools. However, discovering dangerous behaviors, such as fighting and falling down, usually depends on untimely human observations. In this paper, we focus on detecting dangerous behaviors of students automatically, which faces numerous challenges, such as insufficient datasets, confusing postures, keyframes detection and prompt response. To address these challenges, we first build a danger behavior dataset with locations and labels from surveillance videos, and transform action recognition of long videos to an object detection task that avoids keyframes detection. Then, we propose a novel end-to-end dangerous behavior detection method, named DangerDet, that combines multi-scale body features and keypoints-based pose features. We could improve the accuracy of behavior classification due to the highly correlation between pose and behavior. On our dataset, DangerDet achieves 71.0\% mAP with about 11 FPS. It keeps a better balance between the accuracy and time cost.
