{"id":"military-video-analytics","slug":"military-video-analytics","title":"Military Video Analytics","description":"A reconnaissance drone orbiting at 400 metres is streaming live video over a contested urban area. An analyst watching the feed spots what might be a command vehicle parked behind a building. The Military Video Analytics","category":"analytics","tags":["analytics","ai","real-time"],"lastModified":"2026-04-02","source_ref":"content/modules/military-video-analytics.md","url":"/developers/military-video-analytics","htmlPath":"/developers/military-video-analytics","jsonPath":"/api/docs/modules/military-video-analytics","markdownPath":"/api/docs/modules/military-video-analytics?format=markdown","checksum":"5f0210f0d83f2a9afdcf719c5918a0fed593930f906fd642cc531f20db0cf23b","headings":[{"id":"overview","text":"Overview","level":2},{"id":"key-features","text":"Key Features","level":2},{"id":"military-object-detection","text":"Military Object Detection","level":3},{"id":"drone-telemetry-geocoding","text":"Drone Telemetry Geocoding","level":3},{"id":"high-value-target-auto-detection","text":"High-Value Target Auto-Detection","level":3},{"id":"multi-frame-tracking","text":"Multi-Frame Tracking","level":3},{"id":"nato-stanag-alignment","text":"NATO STANAG Alignment","level":3},{"id":"use-cases","text":"Use Cases","level":2},{"id":"integration","text":"Integration","level":2}],"markdown":"# Military Video Analytics\n\n## Overview\n\nA reconnaissance drone orbiting at 400 metres is streaming live video over a contested urban area. An analyst watching the feed spots what might be a command vehicle parked behind a building. The Military Video Analytics module identifies it as a command vehicle within 200ms, geocodes its position from the drone's telemetry, flags it as a high-value target, and submits the grid coordinates to the effector matching engine, all before the analyst reaches for the keyboard. The drone moves on; the track record remains.\n\nThe module extends the platform's video analytics pipeline with specialised military object detection, drone telemetry-based geocoding, and NATO-aligned target classification. It detects and classifies 47 military object classes across five categories, applies a pinhole camera model with heading rotation to geocode detections from drone video using telemetry data, and generates high-value target alerts that feed directly into the effector matching engine. Multi-frame tracking maintains persistent track IDs across video frames for continuous target monitoring.\n\n```mermaid\nflowchart LR\n    A[Drone Video Feed] --> B[Military Object Detector]\n    C[Drone Telemetry] --> D[Pinhole Geocoding Model]\n    B --> E[47-Class Detection Output]\n    D --> F[Georeferenced Position]\n    E --> G[HVT Classifier]\n    F --> G\n    G --> H[High-Value Target Alert]\n    G --> I[Multi-Frame Tracker]\n    H --> J[Effector Matching Engine]\n    I --> K[TAK CoT Event]\n    H --> L[Unified Operational Events]\n    K --> M[ATAK Display]\n```\n\n**Last Reviewed:** 2026-04-02\n**Last Updated:** 2026-04-14\n\n## Key Features\n\n### Military Object Detection\n\nDetect and classify objects across 47 military classes organised into five categories:\n\n- **Vehicles**: Main battle tanks, infantry fighting vehicles, armoured personnel carriers, self-propelled artillery, multiple launch rocket systems, air defence vehicles, armoured reconnaissance vehicles, military trucks, fuel tankers, engineering vehicles, command vehicles, and logistics trailers.\n- **Personnel**: Dismounted infantry, crew-served weapon teams, snipers, forward observers, and command groups.\n- **Aerial**: Fixed-wing aircraft, rotary-wing aircraft, Group 1-5 UAS (by size category), cruise missiles, and parachutes.\n- **Structures**: Defensive fighting positions, bunkers, command posts, supply dumps, antenna arrays, and camouflage netting.\n- **Equipment**: Towed artillery, anti-tank guided missile launchers, man-portable air defence systems, radar arrays, electronic warfare equipment, and communication masts.\n\n### Drone Telemetry Geocoding\n\nConvert pixel-space detections from drone video into geographic coordinates using a pinhole camera model with heading rotation. The geocoding pipeline ingests drone telemetry (latitude, longitude, altitude, heading, pitch, roll, camera field-of-view) and computes the ground intersection point for each detection bounding box centre. The result is a georeferenced detection with estimated position accuracy based on altitude and camera geometry.\n\n### High-Value Target Auto-Detection\n\nAutomatically flag detections matching configurable high-value target criteria. Default HVT classes include air defence systems, command vehicles, electronic warfare equipment, and self-propelled artillery. When an HVT is detected, the system generates an immediate priority alert with geocoded position, classification, confidence score, and a video frame capture. HVT criteria are configurable per organisation.\n\n### Multi-Frame Tracking\n\nMaintain persistent track IDs across consecutive video frames using visual appearance matching and motion prediction. When a detected object moves across the field of view, the tracker assigns and maintains a consistent track ID so analysts can follow individual targets and the system can compute movement vectors. Track persistence through brief occlusions (behind buildings or vegetation) is supported via motion extrapolation.\n\n### NATO STANAG Alignment\n\nTarget classifications align with NATO STANAG 4748 (target classification taxonomy) and are rendered using MIL-STD-2525D military symbology. Detected targets can be exported in STANAG-compliant formats for interoperability with NATO partner systems. Classification labels use NATO standard terminology to ensure unambiguous communication across multinational operations.\n\n## Use Cases\n\n- **Drone Reconnaissance**: Process live drone video feeds to automatically detect and classify military objects on the ground, building an intelligence picture without requiring an analyst to watch every frame.\n- **Strike Target Development**: Geocode detected high-value targets from drone video and feed positions directly to the effector matching engine for engagement option generation.\n- **Route Reconnaissance**: Detect defensive positions, obstacles, and enemy vehicles along planned routes of advance, geocoding each detection for plotting on the operational map.\n- **Battle Damage Assessment**: Analyse post-strike drone video to detect remaining equipment, classify damage states, and report BDA findings with geographic coordinates.\n- **Force Protection**: Monitor base approaches via surveillance drone video with automatic detection of approaching vehicles, personnel, and aerial threats.\n\n## Integration\n\n- **Drone Operations Management**: Receives drone telemetry for geocoding and processes video from managed drone platforms.\n- **Effector Matching Engine**: Geocoded HVT detections are submitted as targets for engagement option scoring.\n- **Unified Operational Events**: All detections and HVT alerts generate events in the unified timeline.\n- **Stone Soup Sensor Fusion**: Video-derived tracks are submitted for multi-sensor fusion with GMTI and acoustic data.\n- **Surveillance AI**: Extends the existing surveillance AI pipeline with military-specific detection models and classification taxonomy.\n- **Tactical Awareness (TAK)**: Geocoded detections are published as CoT events for TAK device display using MIL-STD-2525D symbology.\n\nGraphQL: `militaryDetections`, `militaryDetectionsInArea`, `militaryClasses`, `militaryModels`, `militaryVideoStats` (queries); `ingestMilitaryDetection`, `seedMilitaryClasses`, `toggleMilitaryClass`, `registerMilitaryModel` (mutations).\n"}