Fight Detection

Fight detection research aims to automatically identify instances of fighting in videos and still images, primarily for surveillance and content moderation applications. Current approaches leverage deep learning, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as BiLSTMs), and transformer models, often combined with techniques like anomaly detection and multiple-instance learning to address challenges posed by limited labeled data. This field is significant due to its potential for improving public safety through automated violence monitoring and for enhancing online content moderation by identifying and removing violent material.