Abstract
As violent crimes continue to happen, it becomes necessary to have security cameras that can rapidly identify moments of violence with excellent accuracy. The purpose of this study is to identify how many frames should be analyzed at a time in order to optimize a violence detection model's accuracy as a parameter of the depth of a 3D convolutional network. Previous violence classification models have been created, but their application to live footage may be flawed. In this project, a convolutional neural network was created to analyze optical flow frames of each video. The number of frames analyzed at a time would vary with one, two, three, ten, and twenty frames, and each model would be trained for 20 epochs. The greatest validation accuracy was 94.87% and occurred with the model that analyzed three frames at a time. This means that machine learning models to detect violence may function better when analyzing three frames at a time for this dataset. The methodology used to identify the optimal number of frames to analyze at a time could be used in other applications of video classification, especially those of complex or abstract actions, such as violence.
Abstract (translated)
随着暴力犯罪的持续发生，需要有能够快速且准确识别暴力时刻的安全摄像头。本研究旨在确定为了优化暴力检测模型的准确性，三维卷积网络深度参数下应该一次性分析多少帧。虽然已有一些暴力分类模型被创建出来，但它们在实时视频中的应用可能存在缺陷。在这个项目中，我们建立了一个卷积神经网络来分析每个视频的光流帧。一次分析的帧数分别为一、二、三、十和二十帧，并且每种模型都进行了20个周期的训练。最高的验证准确率达到了94.87%，这发生在一次性分析三帧的模型上。这意味着，对于这个数据集来说，机器学习模型在检测暴力时可能在一次分析三帧的情况下效果更好。用于确定最佳分析帧数的方法也可以应用于其他视频分类领域，特别是那些复杂或抽象动作的分类，如暴力行为识别。
URL
https://arxiv.org/abs/2411.01348
