Utilization of deep learning in PTZ (pan-tilt-zoom) camera control systems for geospatial-based intelligence surveillance

Abstract

Background: The rising complexity of threats to public safety and critical infrastructure has highlighted the limitations of conventional human-operated surveillance systems, creating the need for adaptive, intelligent, and real-time monitoring solutions. Advances in artificial intelligence (AI), computer vision, and geospatial technologies provide opportunities to enhance surveillance through automated detection, analysis, and response. This article examines the integration of pan-tilt-zoom (PTZ) cameras with deep learning models, geospatial data, and distributed computing frameworks as the foundation for next-generation intelligent surveillance systems. Methods: The study employs a narrative review approach, synthesizing recent developments in PTZ camera calibration, convolutional neural networks (CNN), reinforcement learning for autonomous control, and fog computing for distributed video analysis. Research spanning dual-mode fisheye-PTZ systems, lightweight CNN architectures, geospatial data integration, and Internet of Robotic Things (IoRT) frameworks is analyzed to demonstrate practical applications in smart city, industrial, and defense contexts. Findings: Findings reveal that PTZ cameras, when coupled with deep learning and geospatial intelligence, achieve high accuracy in real-time object tracking, small-object recognition, and anomaly detection, with minimal latency under dynamic conditions. Experimental evidence shows error margins below 2% in calibration models and near-perfect accuracy in long-range facial recognition. Integration with fog computing and IoRT enhances responsiveness, scalability, and contextual awareness, while reinforcement learning enables autonomous decision-making for robots and camera networks. Conclusion: The article concludes that combining PTZ hardware precision, AI-based visual analysis, and spatial data intelligence transforms surveillance systems from passive observers into proactive, adaptive, and collaborative agents. However, challenges remain in ensuring robustness under real-world conditions, minimizing latency, and addressing operational usability. Novelty/Originality of this article: This work presents a holistic synthesis of AI-driven vision, PTZ camera control, geospatial intelligence, and distributed architectures, offering an integrated framework for developing adaptive and context-aware surveillance systems in the digital era