Design of an Improved Model for Night Vision Surveillance Using CFMTAN and UG-PSME with Context-Aware Threat and Patrol Intelligence Scenarios

The increasing need for intelligent surveillance systems operating in low-Visibility conditions on critical infrastructures and sensitive border zones creates the demand for autonomous platforms that provide reliable threat assessment and adaptive patrolling. Robotic surveillance solutions currently available suffer mainly due to fragmented sensor fusion, tangible patrol path design, and poor contextual behavior analysis, proving ineffective in dynamic, low-light environments. The current work addresses these limitations in setting forth the design of an integrated Night Vision Patrolling Robot (NVPR) architecture comprising three core modules, each serving its own purpose of surveillance. First, the Context-Fused Multi-Modal Threat Analysis Network (CFMTAN) carries out real-time fusion of infrared (IR) imagery, acoustic signatures, thermal data, and motion signals through a novel Cross-Modal Temporal Attention Layer (CMTAL), enabling context sensitive threat prioritization with adaptive weighting of sensory modalities, yielding high detection accuracy at 93.5% and a latency of less than 300 ms. Further, the Uncertainty-Guided Probabilistic Spatial Mapping Engine (UG-PSME) incorporates odometry, GPS, ultrasonic data, and histories of threats for the creation of patrol paths that evolve along with the apprehended behaviours. Employing the Bayesian Spatial Memory Unit (BSMU), UG-PSME provides a dynamically updated confidence scoring of spatial grids, thus enhancing coverage by 22.3% while sustaining navigation success of over 96.1% in truly uncertain terrains. Finally, the Localized Behavior Deviation Indexing Module (LBDIM) employs human trajectory analysis and zone-specific metadata to identify early behavioral anomalies through a One-Class Deviation Network (OCDN). This will enable precision and context-sensitive deviation scoring with an early detection rate of 89.4% for different scenarios.