ENHANCING PUBLIC SAFETY IN SMART CITIES USING DEEP LEARNING-BASED VIOLENCE DETECTION

Abstract

This work illustrates the utilization of deep learning to detect violent incidents in smart cities, thereby improving public safety. Automated threat identification and real-time surveillance research are implemented by the technology. The proposed method employs sophisticated convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze video feeds from public surveillance cameras, accurately detecting violent incidents such as altercations, assaults, vandalism, and mass violence. The system's proficiency in distinguishing between violent and non-aggressive human behaviors across a variety of external conditions is attributed to its implementation of spatial and temporal feature extraction techniques. The model is trained using a vast array of annotated datasets. The goals are to improve the effectiveness of detection, decrease the number of false alarms, and facilitate the rapid response of law enforcement. The proposed method improves the efficiency of emergency response, reduces the need for human supervision, and enhances smart city monitoring by providing continuous updates on current events. The intelligence, safety, and security of urban areas can be substantially improved by this violence detection system, which employs deep learning.