Optimizing Violence Detection in Video Classification Accuracy via 3D Convolutional Neural Networks
Year: 2025
Type: article
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.
Source: Journal of Student Research
Authors: Aarjav Kavathia, Simeon Sayer
Institution: Harvard University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Harvard University
OpenAlex ID: W4413565523