Deep Learning for Image and Video Processing in Surveillance Systems: Advancing Security with AI Driven Insights

The incorporation of deep learning in surveillance system has significantly enhanced image and video processing thus improving real-time and accurate security System. The following work introduces a novel deep learning solution which aims to improve object recognition, anomalous behavior identification, and privacy preservation related to surveillance systems. The improved proposed system gave a precision of 97.5% while the recall was at 96.3% and F1-score of the system was 96.9% The performance of the proposed system outcompeted the current benchmarks such same as YOLOv5, Faster R-CNN and SSD. In real-time, the efficiency of real-time processing was accomplished by the use of a parallel, direct hybrid model with a latency level of 15 ms and bandwidth consumption of 3 Mbps with an accuracy of 96.7% in the provided experiment. Initial experiments of the anomaly detection scenarios including the details of a suspect object, unauthorised entry, and threatening actions successfully achieved average detection rates between 96.6% and 97.3% with only 2.1 % of false positive and negative rates down to 0.2. Privacy measures successfully maintained data encryption at 98.7% and GDPR/GDPR compliant data at 96.2%. These findings highlight the efficiency of the system in delivering large-scale, energy-responsive, and ethically sound surveillance opportunities for various contexts, opening the direction for AI-enhanced public safety and security surveillance progression.