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Modern surveillance systems collect enormous amounts of visual information, but turning that data into actionable intelligence remains a challenge. Operators monitoring borders, military bases, or critical infrastructure must distinguish genuine threats from harmless activity, often in poor weather, low light, or visually cluttered environments. False alarms can slow decision-making, while missed detections may have operational consequences.
A new software platform (Prism Ground ISR by Teledyne FLIR OEM) aims to improve that process by combining thermal imaging, visible-light cameras, and artificial intelligence into a single target detection and tracking system.
Designed for intelligence, surveillance, and reconnaissance (ISR) missions, the platform analyzes data from both electro-optical and infrared sensors to detect, classify, and continuously track ground-based targets. Instead of relying solely on raw camera feeds, AI models process the imagery to identify objects and distinguish between different categories, including specific military vehicle types.
One of the platform’s primary goals is reducing false alarms while improving detection accuracy. By automatically classifying objects before presenting them to operators, the system helps prioritize potential threats and reduces the workload associated with manually reviewing surveillance footage.
To improve image quality, the software incorporates several computational imaging techniques. Turbulence mitigation compensates for atmospheric distortion, dehazing improves visibility through smoke or haze, and super-resolution enhances image detail beyond the native resolution of the sensor. Together, these capabilities make it easier to identify distant or partially obscured objects.
According to Interesting Engineering. the platform also separates target acquisition from target tracking. This allows the software to continue following moving vehicles or personnel even after new objects enter the scene, improving tracking persistence during complex surveillance operations.
From a defense perspective, AI-assisted surveillance is becoming an increasingly important component of border security, force protection, and battlefield awareness. Combining thermal and visible-spectrum imagery enables continuous monitoring during both daylight and nighttime operations, while automated classification accelerates decision-making in rapidly changing environments.
The software currently supports recognition of multiple object categories and has been trained using both real-world and synthetic electro-optical and infrared datasets. Additional target classes can be added without collecting large volumes of new imagery by generating synthetic training data.
Compatible with several thermal camera families and edge-computing platforms, the software is designed for integration into existing surveillance systems rather than requiring entirely new hardware.
As military sensing increasingly shifts toward software-defined capabilities, combining AI, computational imaging, and multi-sensor fusion is enabling surveillance systems to detect threats earlier while delivering more relevant information to operators in real time.
