Predictive Quality Management in Nepalese Multi-Modal Sensing with Deep Learning for Real-Time Human Behavior Recognition in Drone Surveillance

Abstract

The rapid advancement of drone technology has opened new opportunities for intelligent surveillance. However, most existing drone systems are limited by their reliance on single-mode visual inputs, which often fails to capture the nuances of complex human behaviors critical for identifying security threats. This research addresses these limitations by proposing a novel multi-modal sensing framework that integrates data from RGB cameras, infrared thermal sensors, and motion detectors. Unlike conventional systems, this approach creates a richer and more comprehensive representation of human activity, significantly improving situational awareness. A hybrid deep learning model, combining convolutional and recurrent neural networks, is employed to process this multi-modal input in real time, allowing for the accurate recognition of both normal and abnormal behavioral patterns. The system is designed to quickly identify suspicious activities—such as erratic gestures or signs of aggression—even in challenging conditions like poor lighting or occlusions. To ensure practical applicability, the framework's robustness and reliability are validated through real-world field experiments. Ultimately, this study contributes to the development of autonomous, intelligent drone surveillance systems that not only strengthen current security capabilities but also lay a foundation for future innovations in AI-driven public safety technologies.