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Technical Overview

How the ReCAP platform was designed to automatically analyse and extract metadata from live broadcast-quality video and archive content.

Platform Design

The ReCAP platform was conceived as an affordable, scalable automatic content analysis service. It targeted real-time processing of live broadcast-quality video alongside retrospective analysis of archive content, producing time-stamped descriptive and technical metadata to enrich media workflows.

By integrating directly into existing Media Asset Management and quality assurance pipelines, the platform aimed to reduce manual review effort and surface actionable information — from content characteristics to technical integrity issues — without requiring enterprise-scale infrastructure.

Abstract visualization of video frames flowing through a digital analysis pipeline with waveform overlays in blue and teal tones

Analysis Capabilities

The platform's planned feature set combined content-recognition services with automated quality control monitoring. A working prototype demonstrated several of these capabilities at NAB 2017.

Logo Detection

Automatic identification and time-stamping of broadcast logos and brand marks within video streams, enabling rights tracking and compliance verification.

Face Detection

Detection of human faces in footage with frame-accurate timecode, supporting content indexing and editorial search workflows.

Content Duplication Detection

Identification of repeated or near-duplicate segments across archives and incoming feeds, helping to reduce storage waste and flag syndicated material.

Automated Quality Control

Beyond content recognition, ReCAP incorporated technical quality monitoring designed to catch common broadcast and post-production issues automatically:

Frame Integrity

Detection of lost or frozen video frames that could indicate transmission errors or encoding faults.

Signal Dropouts

Identification of momentary signal loss events that produce black or corrupted frame sequences.

Macroblocking Detection

Recognition of visible compression artefacts such as macroblocking, common in over-compressed or bandwidth-starved streams.

Sharpness & Noise Estimation

Quantitative estimation of perceived sharpness and noise levels, providing objective metrics for quality assurance teams.

Metadata-Driven Workflows

All extracted data — both descriptive and technical — was designed to be delivered as time-stamped metadata, compatible with standard Media Asset Management systems. This allowed facilities to enrich existing catalogues, trigger automated QC alerts, and build smarter search and retrieval tools without replacing their established infrastructure.

The platform's architecture targeted heterogeneous production environments, supporting multiple source formats and playout targets common in modern broadcast and post-production facilities.

🇪🇺 This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 732461.