AI-Driven Physics-Based Simulation Design with Optical Flow Based Markov Decision Processes for Smart Surveillance

This paper presents an integrated approach to enhancing situational awareness and decision-making in dynamic environments by combining optical-flow-based Markov Decision Processes (MDP) with physicsbased simulations for proactive surveillance system design. By utilizing optical flow for real-time motion detection and analysis, our framework achieves immediate comprehension of environmental changes, which is essential for autonomous navigation and surveillance applications. Additionally, the framework employs MDP to model and resolve decision-making problems where outcomes are partly random and partly controlled by the decision-maker, optimizing actions based on predicted future states. The model was validated through Hardware-in-the-loop (HITL) simulations, providing a detailed understanding of the physical phenomena influencing the system. This ensures that decisions are data-driven and customized to specific situations and missions. Our approach offers a comprehensive understanding of system integration with AI, along with real-time analysis and decision-making capabilities, thereby advancing the simulation modeling methodology for engaging with complex, dynamic environments.