Cloud-Enabled Deep Learning Framework for Public Security Video Investigation Systems

The incorporation of deep learning methods into public security video investigation systems is investigated in this review article with special attention to their transforming ability in improving real-time surveillance and crime prevention. With the rapid developments in machine learning and computer vision, deep learning models which includes Convolutional Neural Networks, and Recurrent Neural Networks (RNNs) have shown astonishing capacity in automating video surveillance tasks including finding objects, activity recognition, and anomaly detection. These models are highly useful for public safety operations since they enable crowd management, identification of suspicious behaviour, and even specific actions like theft or assault. Examining the technical architecture of these systems, the paper emphasises on the part edge computing and cloud computing play in allowing scalability and real-time data processing. While edge computing provides localised processing to lower latency and increase response times, cloud-based solutions guarantee perfect integration and storage of vast video information. Moreover, the study tackles the difficulties in applying deep learning in public security including privacy issues, data security, ethical questions, and the necessity of laws. Notwithstanding these difficulties, the research underlines how these technologies might help to enhance security operations, lower human error, and raise operational efficiency.