通过3D卷积网络优化暴力检测，发现分析三帧时准确率最高。
Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks
- 用3D卷积网络分析不同数量的光流帧，寻找最佳帧数
- 分析三帧时验证准确率达94.87%，为最高值
- 方法可推广至复杂动作识别任务，适合安全监控场景
随着暴力犯罪持续发生，亟需具备快速高精度识别暴力时刻的监控系统。本研究旨在确定3D卷积神经网络深度参数下，每次应分析多少帧以优化暴力检测模型的准确率。现有暴力分类模型在实时视频中应用可能存在缺陷。本项目构建卷积神经网络，分析每段视频的光流帧，分别测试一次分析1、2、3、10、20帧的情况，各模型训练20个周期。最高验证准确率为94.87%，出现在分析三帧的模型上，表明针对该数据集，每次分析三帧可实现最佳性能。该方法可拓展至其他复杂或抽象动作的视频分类任务。
原文摘要 · Abstract (English)
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.
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