AUTOMATED VIOLENCE RECOGNITION IN SMART CITIES USING ADVANCED DEEP LEARNING TECHNIQUES

Abstract

An automated framework for violence recognition is introduced in this research. It employs state-of-the-art deep learning algorithms to improve public safety in smart cities using analytics derived from real-time video surveillance. In order to successfully detect violent incidents including riots, physical assaults, and unexpected crowd hostility, the suggested method extracts spatial and temporal information from surveillance footage and merges Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. Using attention techniques for improved feature representation and transfer learning with pre-trained architectures, the model successfully differentiates violent events from random human interactions in a variety of illumination, occlusion, and crowd-density scenarios. The computational efficiency is maintained while achieving great accuracy, precision, and recall, as demonstrated experimentally on benchmark datasets. This makes it an ideal candidate for smart city infrastructure that is situated on the edge. This research improves IUSS by developing a proactive, scalable, and automated method for detecting violent incidents; this allows for quicker responses and better crime prevention measures.