Development of Intelligent Video Surveillance System Using Deep Learning and Convolutional Neural Networks: A Proactive Security Solution

The combination of innovative criminal activity detection systems with deep learning technologies has transformed public safety and surveillance tools. This study presents the expansion and experimental evaluation of an intelligent video surveillance system that integrates facial recognition, activity recognition and weapon detection using recurrent neural networks (RNNs), convolutional neural networks (CNNs) and YOLO-based models. Unlike purely review-based contributions, this work combines a comprehensive survey of existing methodologies with the design and implementation of a prototype system, validated through real-time video surveillance experiments. The proposed system demonstrates high accuracy in detecting faces, weapons, and suspicious activities, supported by a graphical user interface for practical deployment. Significant barriers include performance inefficiencies and ethical implications, and dataset biases are also critically analyzed. The findings highlight both the technical effectiveness of the system and the broader implications for scalable, automated, and ethically responsible surveillance solutions in the context of Industry 4.0.