School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This paper proposes a video-based school violence detecting algorithm. This algorithm first detects foreground moving targets via the KNN (K-Nearest Neighbor) method and then preprocesses the detected targets via morphological processing methods. Then, this paper proposes a circumscribed rectangular frame integrating method to optimize the circumscribed rectangular frame of moving targets. Rectangular frame features and optical-flow features were extracted to describe the differences between school violence and daily-life activities. We used the Relief-F and Wrapper algorithms to reduce the feature dimension. SVM (Support Vector Machine) was applied as the classifier, and 5-fold cross validation was performed. The accuracy was 89.6%, and the precision was 94.4%. To further improve the recognition performance, we developed a DT-SVM (Decision Tree-SVM) two-layer classifier. We used boxplots to determine some features of the DT layer that are able to distinguish between typical physical violence and daily-life activities and between typical daily-life activities and physical violence. For the remainder of activities, the SVM layer performed a classification. For this DT-SVM classifier, the accuracy reached 97.6%, and the precision reached 97.2%, thus showing a significant improvement.

中文翻译：

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一种基于视频的DT-SVM学校暴力检测算法。

学校欺凌是青少年中的一个严重问题。学校暴力是学校欺凌行为的一种，被认为是最有害的。随着AI（人工智能）技术的发展，现在出现了检测学校暴力的新方法。本文提出了一种基于视频的学校暴力检测算法。该算法首先通过KNN（K最近邻）方法检测前景移动目标，然后通过形态学处理方法对检测到的目标进行预处理。然后，提出了一种外接矩形框积分方法，以优化运动目标的外接矩形框。提取矩形框特征和光流特征来描述学校暴力与日常生活活动之间的差异。我们使用了Relief-F和Wrapper算法来缩小特征尺寸。应用SVM（支持向量机）作为分类器，并进行5倍交叉验证。精度为89.6％，精度为94.4％。为了进一步提高识别性能，我们开发了DT-SVM（决策树-SVM）两层分类器。我们使用箱形图来确定DT层的某些功能，这些功能可以区分典型的身体暴力和日常生活活动以及典型的日常生活活动和身体暴力。对于其余活动，SVM层执行了分类。对于该DT-SVM分类器，准确度达到97.6％，准确度达到97.2％，因此显示出显着的提高。并进行5倍交叉验证。精度为89.6％，精度为94.4％。为了进一步提高识别性能，我们开发了DT-SVM（决策树-SVM）两层分类器。我们使用箱形图来确定DT层的某些功能，这些功能可以区分典型的身体暴力和日常生活活动以及典型的日常生活活动和身体暴力。对于其余活动，SVM层执行了分类。对于该DT-SVM分类器，精度达到97.6％，精度达到97.2％，显示出显着的提高。并进行5倍交叉验证。精度为89.6％，精度为94.4％。为了进一步提高识别性能，我们开发了DT-SVM（决策树-SVM）两层分类器。我们使用箱形图来确定DT层的某些功能，这些功能可以区分典型的身体暴力和日常生活活动以及典型的日常生活活动和身体暴力。对于其余活动，SVM层执行了分类。对于该DT-SVM分类器，精度达到97.6％，精度达到97.2％，显示出显着的提高。我们使用箱形图来确定DT层的某些功能，这些功能可以区分典型的身体暴力和日常生活活动以及典型的日常生活活动和身体暴力。对于其余活动，SVM层执行了分类。对于该DT-SVM分类器，精度达到97.6％，精度达到97.2％，显示出显着的提高。我们使用箱形图来确定DT层的某些功能，这些功能可以区分典型的身体暴力和日常生活活动以及典型的日常生活活动和身体暴力。对于其余的活动，SVM层执行了分类。对于该DT-SVM分类器，精度达到97.6％，精度达到97.2％，显示出显着的提高。