AI Video Analytics: From Footage to Metrics
AI video analytics is the quantitative layer of video intelligence — extracting numbers from footage. Cycle time, player load, dwell time, utilization rate, compliance score. Metrics that previously required sensors, surveys, or manual observation now come from video.
Analytics by vertical
| Vertical | Key metrics | Export formats |
|---|---|---|
| Sports | Distance, sprint count, possession %, heatmap, pressing intensity, formation adherence | PDF report, Excel, JSON via API |
| Industrial (TMA) | Cycle time, idle time %, VA/NVA ratio, OEE Performance, utilization rate, SOP compliance | PDF report, Excel, JSON via API |
| Forensic | Person count, dwell time, crowd density, zone entries/exits, movement speed | PDF evidence report, CSV timeline, frame extracts |
Report types in VidForgeX
Single-analysis deep dive: all metrics, event timeline, person-level breakdown, AI insights, and coaching points. Available as branded PDF.
Aggregated metrics across all sessions in a period. Identifies trends, outliers, and team-level patterns. Configurable date range (1–365 days).
Individual performance over time. Charts key metrics session-by-session with AI-generated trend commentary. Up to 50 sessions.
Side-by-side comparison of 2–10 persons or sessions. Ranks by any metric, highlights gaps and similarities.
The data pipeline behind analytics
Analytics data flows from raw AI inference through structured schemas:
- Raw inference — AI models output structured JSON per analysis type
- Schema validation — output validated against typed schemas per analysis mode
- Metric extraction — numeric metrics extracted into a time-series store (one record per video × analysis type)
- Aggregation — team summaries and person progressions computed via SQL aggregation over stored metrics
- Presentation — frontend renders via real-time query; API serves via MCP tools and REST endpoints