Detecting Violence in Videos using Convolutional Neural Networks

This paper explores the practical implementation of 3D Convolutional Neural Networks (CNNs) for real-time violence detection in surveillance scenarios, focusing on applications such as traffic aggression and bullying detection. In recent years, the proliferation of surveillance cameras has provided a vast amount of visual data, necessitating efficient and accurate automated methods for threat identification. In this study, we investigate the feasibility and effectiveness of employing 3D CNNs for violence detection in real-life scenarios. We propose a comprehensive framework that integrates data augmentation techniques and fine-tuning strategies to address the challenges of limited annotated data and diverse environmental conditions. Furthermore, we conduct extensive experiments on benchmark datasets to evaluate the performance of the proposed approach in detecting physical altercations. In summary, this study highlights the efficacy of 3D CNNs in detecting physical altercations, thereby contributing to the advancement of automated surveillance systems aimed at enhancing public safety. The findings presented herein not only underscore the practical utility of deep learning techniques in addressing real-world challenges but also point towards future avenues for improving the accuracy and reliability of violence detection algorithms through multimodal integration.