Violence Detection Model

A vital field of machine intelligence with uses in surveillance and public safety is violence detection. The accuracy and limitations of several models, such as SVM, 3-D CNN,oriented violent flow , Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid techniques, are examined in this paper. Attention methods and transformer-based topologies have demonstrated the potential to enhance detection performance. Despite progress, challenges including dataset bias, environmental changes, and high processing costs still exist. Real-time processing and attaining robustness in a variety of settings are challenges for many models. This study compares the pros and cons of the current methodology through an organized evaluation. We provide a explanatory evaluation of their accuracy using established datasets available on the internet. We present extensive research of their accuracy on popular datasets. This report also highlights drawbacks in the current body of knowledge and suggests future direction. Reliability, scalability, and efficiency must be improved for systems that detect anomalies.