Military Video Analytics

Overview#

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 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.

The 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.

Open Standards#

• NATO STANAG 4748 (Target Classification Taxonomy): Detection classes and their labels align with the STANAG 4748 taxonomy, ensuring target nomenclature is unambiguous across multinational operations; each registered class carries a nato_stanag_code field for direct mapping.
• MIL-STD-2525D (Common Warfighting Symbology): Geocoded detections published to TAK devices use MIL-STD-2525D symbol identifiers so that targets are rendered with standard military symbology on ATAK, WinTAK, and compatible situational-awareness displays.
• NATO Cursor-on-Target (CoT): Video-derived tracks and high-value target alerts are serialised as CoT XML events and published to TAK infrastructure, using the CoT 2.0 event envelope that underpins ATAK and allied TAK-family systems.
• NATO STANAG 4609 / MISB ST 0601 (Motion Imagery): Imagery metadata accompanying drone video is ingested and exported in the STANAG 4609 format using MISB ST 0601 KLV local-set encoding, allowing sensor-metadata correlation (platform position, sensor pointing, timestamp) to be preserved alongside detections.
• NATO STANAG 4607 (GMTI): When fused with the Stone Soup sensor fusion layer, video-derived tracks are merged with STANAG 4607 Ground Moving Target Indicator reports, producing a unified multi-sensor track picture from both video and radar sources.
• WGS-84 (World Geodetic System 1984): All geocoded detection positions (latitude, longitude, altitude) are expressed in WGS-84 coordinates, consistent with the datum used by drone telemetry, STANAG 4607 positional fields, and NATO operational contracts.
• GraphQL: The module exposes its full query and mutation surface over a GraphQL API (militaryDetections, militaryDetectionsInArea, ingestMilitaryDetection, and related operations), enabling interoperable integration with any GraphQL-capable client.

Last Reviewed: 2026-04-02 Last Updated: 2026-04-14

Key Features#

Military Object Detection#

Detect and classify objects across 47 military classes organised into five categories:

• 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.
• Personnel: Dismounted infantry, crew-served weapon teams, snipers, forward observers, and command groups.
• Aerial: Fixed-wing aircraft, rotary-wing aircraft, Group 1-5 UAS (by size category), cruise missiles, and parachutes.
• Structures: Defensive fighting positions, bunkers, command posts, supply dumps, antenna arrays, and camouflage netting.
• Equipment: Towed artillery, anti-tank guided missile launchers, man-portable air defence systems, radar arrays, electronic warfare equipment, and communication masts.

Drone Telemetry Geocoding#

Convert 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.

High-Value Target Auto-Detection#

Automatically 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.

Multi-Frame Tracking#

Maintain 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.

NATO STANAG Alignment#

Target 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.

Use Cases#

• 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.
• Strike Target Development: Geocode detected high-value targets from drone video and feed positions directly to the effector matching engine for engagement option generation.
• Route Reconnaissance: Detect defensive positions, obstacles, and enemy vehicles along planned routes of advance, geocoding each detection for plotting on the operational map.
• Battle Damage Assessment: Analyse post-strike drone video to detect remaining equipment, classify damage states, and report BDA findings with geographic coordinates.
• Force Protection: Monitor base approaches via surveillance drone video with automatic detection of approaching vehicles, personnel, and aerial threats.

Integration#

• Drone Operations Management: Receives drone telemetry for geocoding and processes video from managed drone platforms.
• Effector Matching Engine: Geocoded HVT detections are submitted as targets for engagement option scoring.
• Unified Operational Events: All detections and HVT alerts generate events in the unified timeline.
• Stone Soup Sensor Fusion: Video-derived tracks are submitted for multi-sensor fusion with GMTI and acoustic data.
• Surveillance AI: Extends the existing surveillance AI pipeline with military-specific detection models and classification taxonomy.
• Tactical Awareness (TAK): Geocoded detections are published as CoT events for TAK device display using MIL-STD-2525D symbology.

GraphQL: militaryDetections, militaryDetectionsInArea, militaryClasses, militaryModels, militaryVideoStats (queries); ingestMilitaryDetection, seedMilitaryClasses, toggleMilitaryClass, registerMilitaryModel (mutations).