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    AI Video Analytics

    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

    VerticalKey metricsExport formats
    SportsDistance, sprint count, possession %, heatmap, pressing intensity, formation adherencePDF report, Excel, JSON via API
    Industrial (TMA)Cycle time, idle time %, VA/NVA ratio, OEE Performance, utilization rate, SOP compliancePDF report, Excel, JSON via API
    ForensicPerson count, dwell time, crowd density, zone entries/exits, movement speedPDF evidence report, CSV timeline, frame extracts

    Report types in VidForgeX

    Session Report

    Single-analysis deep dive: all metrics, event timeline, person-level breakdown, AI insights, and coaching points. Available as branded PDF.

    Team Summary

    Aggregated metrics across all sessions in a period. Identifies trends, outliers, and team-level patterns. Configurable date range (1–365 days).

    Person Progression

    Individual performance over time. Charts key metrics session-by-session with AI-generated trend commentary. Up to 50 sessions.

    Comparison Report

    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:

    1. Raw inference — AI models output structured JSON per analysis type
    2. Schema validation — output validated against typed schemas per analysis mode
    3. Metric extraction — numeric metrics extracted into a time-series store (one record per video × analysis type)
    4. Aggregation — team summaries and person progressions computed via SQL aggregation over stored metrics
    5. Presentation — frontend renders via real-time query; API serves via MCP tools and REST endpoints
    AI video analyticsvideo metricssports analyticscycle time analyticsdwell time analyticsvideo dashboardOEE analyticspossession analyticsVidForgeX analyticsvideo KPIs

    Frequently Asked Questions

    What metrics can AI video analytics measure?
    Sports: distance covered, sprint count, possession %, pressing intensity, zone occupancy. Industrial: cycle time, idle time %, utilization rate, OEE Performance component, VA/NVA ratio, SOP compliance score. Security: person count, dwell time, crowd density, zone entry/exit rates, queue length. All without sensors or manual logging.
    How accurate are AI video analytics metrics?
    Accuracy depends on footage quality, camera angle, and scene complexity — counts and timings degrade in crowded or occluded scenes. Compliance scores are based on detected step completion, and accuracy varies with the visual clarity of the SOP steps. Validate metrics against a manual sample from your own footage before relying on them operationally.
    Can VidForgeX video analytics integrate with BI tools?
    Yes. Analytics data exports as CSV from the Reports page, and is accessible programmatically via the MCP API (get_session_result, get_team_summary tools) and REST API. The JSON structure is documented in the API docs, enabling integration with PowerBI, Tableau, Looker, or custom dashboards.
    Is historical trend analysis possible?
    Yes. VidForgeX stores all analysis results with timestamps. The Team Summary MCP tool returns aggregated metrics over configurable periods (1–365 days). Person Progression shows individual metric trends across sessions. Excel reports include time-series data for import into spreadsheet analytics tools.