Authors: Shivam Namdev, Bhanu Pratap Singh
Abstract: The rapid increase in urbanization, public security challenges, and criminal activities has accelerated the development of intelligent surveillance systems for real-time violence detection and criminal activity identification. Traditional surveillance systems often depend heavily on manual monitoring, which limits detection efficiency, increases response time, and reduces reliability in complex environments. Recent advancements in deep learning, machine learning, computer vision, sensor networks, and predictive analytics have significantly improved automated surveillance capabilities for public safety management. This review presents an intelligent deep learning framework for violence detection and criminal activity identification in smart surveillance systems by analyzing recent developments in convolutional neural networks (CNNs), 3D-CNNs, ConvLSTM architectures, transfer learning, optimization techniques, and sensor-based monitoring systems. The framework integrates video analytics, spatiotemporal feature extraction, facial recognition, object detection, anomaly detection, and predictive threat analysis into a unified intelligent surveillance ecosystem. Furthermore, the study highlights the role of real-time monitoring, smart city technologies, and intelligent decision-support systems in improving public security operations. The review indicates that deep learning-based surveillance frameworks significantly improve violence detection accuracy, reduce false alarms, enhance predictive threat identification, and support automated emergency response systems in modern smart environments.
