Hybrid Security Systems: Human and Automated Surveillance Approaches

Abstract

The study investigates the performance of hybrid security systems under different personnel training and artificial intelligence (AI) assistance conditions. The aim is to understand the system’s impact on different scenarios that involve human operators and AI and to develop a predictive model for optimizing system performance. A human security information model was built to predict the performance of hybrid security systems. The system’s performance metrics (response time, hits, misses, mistakes), cognitive load, visual discrimination, trust, and confidence were measured under different training and assistance conditions. Participants were divided into trained and non-trained groups, and each group performed surveillance tasks with and without AI assistance. Predictive modeling was performed using Linear Regression. The training significantly improved performance by reducing misses and mistakes and increasing hits, both with and without AI assistance. In the non-trained group, AI assistance boosted speed and hit accuracy but led to more mistakes. AI assessment reduced response time and misses for the trained group while increasing hits without affecting the mistake rate. Trust and confidence were higher with AI in the non-trained group, while AI reduced cognitive load in the trained group. The findings highlight the interactions between human operators, AI assistance, and training in hybrid surveillance systems. The predictive model can guide the design and implementation of these systems to optimize performance. Future studies should focus on strategies to enhance operator trust in AI-assisted systems and confidence, further optimizing the collaborative potential of hybrid surveillance frameworks.