Proposal to Improve The Classification of School Violence

Nowadays, violence in movies and in society is on the rise, which has a significant impact on children, particularly adolescents. The prevalence of school violence is increasing and it is becoming a concern for schools, families, and society as a whole. However, because the school violence detection system has not yet been developed, our lab created VSiSGU data based on the collection of camera data from within the school as well as data from social networks. There are also many techniques for processing continuous image sequence data from cameras in order to detect school violence. As a result, we propose a method for improving performance by selecting frames at the l, l+k, l+2k,..., l+nk positions in the videos to train. After that, we use the VGGNet algorithm combined with RNN to develop a training model on the above data. The evaluation results show that our proposed method is more efficient in terms of time and still ensures higher or equivalent accuracy than the traditional sampling method.