Violence Detection

Violence detection research aims to automatically identify violent acts in video and text data, primarily for enhancing security and safety in public spaces. Current efforts focus on developing robust and efficient deep learning models, often employing multimodal approaches (combining audio, video, and text) and architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), vision transformers (ViTs), and attention mechanisms to improve accuracy and reduce computational costs. This field is significant due to its potential for improving public safety through automated surveillance and content moderation, while also presenting challenges in addressing issues like bias, privacy, and the need for large, diverse datasets.