ReCAP And Dalet Galaxy For Smarter MAM Workflows

Media organisations are under pressure to make every frame searchable, reusable and commercially valuable. A live broadcast may need rapid quality checks, while the same recording later becomes part of an archive, a highlights package or a compliance review. Manual logging rarely keeps pace with that volume, particularly when production teams are working across news, sport, entertainment and digital publishing.

ReCAP offers a practical route towards automated video intelligence by extracting metadata, monitoring technical quality, recognising faces and logos, and identifying duplicated material. Integrated with Dalet Galaxy, those capabilities can become part of the media asset management workflow rather than a separate research tool. The result is a more connected path from ingest and production through to archive enrichment, editorial discovery and reuse.

Why The Integration Matters

Dalet Galaxy can act as the operational centre for content management, including assets, workflows, users, rights information and editorial processes. ReCAP can supply machine-generated observations about the material stored or managed within that environment. Together, the systems can reduce the amount of repetitive logging required from producers, media managers and archivists.

The value is greatest when analysis is treated as structured metadata rather than a report that sits outside the MAM. A detected logo, face, scene change, silence, technical fault or duplicate segment can be linked to the relevant asset and timecode. Editors can then search or filter content using information that would be difficult and expensive to enter manually.

For an Australian broadcaster, this has clear relevance across long production cycles and geographically dispersed operations. A network may ingest material in Sydney, produce a segment in Melbourne and deliver content to regional teams in Queensland, Western Australia or the Northern Territory. Consistent automated metadata helps those teams work from the same description of the media.

A Practical Data Exchange Model

An integration can be designed around a controlled exchange between Dalet Galaxy and ReCAP. Galaxy identifies an asset or collection that requires analysis, then passes a reference and selected technical details to the ReCAP processing environment. ReCAP analyses the media and returns results in a format that can be mapped to Galaxy fields, markers, taxonomies or workflow states.

The exchange may use APIs, message queues, watched storage locations or a combination of these methods, depending on the installation. The important principle is that each result should retain a stable asset identifier, a time range where appropriate, a confidence score and the model or analysis type that produced it. This makes machine-generated information traceable and easier to revise when requirements change.

A returned result might classify a clip as containing a particular sponsor logo, identify a recognised person, flag a section with frozen frames or compare the material with an existing archive item. Galaxy can then store the result as searchable metadata, create a review task or route the asset into another workflow. The project objectives describe the wider research direction behind these capabilities, including real-time analysis and processing for broadcast-quality video.

Metadata That Supports Daily Production

The first useful layer is descriptive metadata. Speech transcription, named entities, detected faces, brands, objects and visual concepts can improve search and assist producers assembling a package under deadline. A newsroom editor looking for previous footage of a flood, a minister or a particular venue could use automated tags to narrow thousands of assets before reviewing the actual pictures.

Quality metadata is equally important. ReCAP-style analysis can identify black frames, excessive motion, audio discontinuities, focus problems, compression artefacts or other issues that affect delivery. These findings can be attached to timecoded markers so an operator can inspect the exact section instead of watching an entire programme from beginning to end.

This approach is valuable for Australian sport and live events, where long recordings often contain material that is reused in short windows. AFL, cricket, tennis and racing libraries can grow quickly, and content may be repackaged for broadcast, streaming, social channels and news bulletins. Automated markers give rights and editorial teams a faster way to locate relevant moments without treating every asset as an unstructured file.

Automation Across The MAM Lifecycle

Integration should begin at ingest, when a media asset receives its initial identity and technical profile. Galaxy can trigger analysis for selected content based on source, programme, workflow type or delivery priority. A live news clip might receive a fast analysis pass, while a completed documentary could enter a deeper archival process after its master file is approved.

Analysis results can then support several downstream actions. A quality warning may send the asset to a technical operator. A probable duplicate may be routed for editorial review before storage costs increase. A recognised logo may help commercial teams find sponsorship exposure, while face recognition can support archive discovery subject to permissions, privacy controls and organisational policy.

Useful workflow triggers include:

The workflow should distinguish between an automated suggestion and a confirmed editorial fact. A confidence score can help determine whether Galaxy applies a tag automatically, presents it for review or keeps it hidden from general users until a specialist approves it.

Real-Time Monitoring For Live Operations

Real-time processing is particularly relevant to live broadcast environments, where there may be no opportunity for a traditional logging pass. Analysis can run during ingest or close to live, producing alerts when a feed develops a visible fault, loses expected branding or contains repeated material. Operators can use these signals alongside existing monitoring systems rather than replacing established broadcast controls.

For a network covering an event in Brisbane or Perth while editorial teams work in Sydney, shared alerts and timecoded evidence can reduce the need for lengthy phone or messaging exchanges. An operator can see whether a suspected fault is a genuine issue, identify its duration and attach the finding to the asset record. This is especially useful when a programme moves rapidly from live transmission into catch-up, clips or highlights production.

Real-time detection also needs sensible thresholds. A camera pan should not be treated as a failure, and a brief logo obstruction may be acceptable in editorial footage. Rules can vary by channel, programme and delivery destination, with Galaxy holding the operational context and ReCAP supplying the analysis. This separation allows technical teams to adjust workflow policy without rebuilding the underlying detection service.

Governance, Privacy And Australian Context

Face recognition and identity-related metadata require stronger governance than ordinary scene labels. Organisations should define which sources may be analysed, who can view the results, how long information is retained and when human verification is mandatory. A detected face should not automatically become a confirmed identity, particularly in news footage involving members of the public, children or sensitive events.

Australian media organisations also work within a landscape shaped by privacy expectations, contractual restrictions, rights agreements and different internal policies across public, commercial and community broadcasters. Content captured for ABC, SBS, a metropolitan network or a regional station may carry different permissions for analysis and reuse. The integration should therefore preserve provenance, access controls and audit records within the MAM workflow.

Practical governance controls include:

Quality and duplication analysis generally presents a different risk profile, but it still needs review. A similar-looking clip may be a legitimate alternate edit, a legally distinct version or a high-value master. Automated detection should accelerate comparison, not silently delete or overwrite material.

Measuring Value Across Teams

A successful integration should be assessed through operational outcomes rather than the number of detections produced. Useful measures include the time saved during logging, the percentage of assets with searchable metadata, the reduction in avoidable quality errors and the speed at which editors locate approved footage. These measures can be compared across production teams before and after deployment.

Search success is another valuable indicator. If an editor can find the right sequence using terms, faces, logos or timecoded events, the archive becomes more useful without requiring every asset to be manually described. Retention and reuse may increase because older material becomes discoverable to teams that did not create it.

A pilot should use representative media rather than a clean demonstration reel. Include live captures, archival files, multi-camera sport, low-light interviews, advertisements, foreign-language segments and content with sensitive imagery. Australian broadcasters also need to account for variable connectivity between metropolitan facilities and regional locations, as well as storage and transfer costs for high-resolution masters.

The ReCAP project provides the broader context for a research initiative focused on extracting actionable information from video. For a Dalet Galaxy deployment, that research can be translated into carefully bounded services, measurable workflow steps and metadata policies suited to the organisation’s own archive.

A Controlled Path From Pilot To Production

The strongest implementation begins with a narrow workflow and a defined business outcome. For example, a broadcaster might analyse incoming sports packages for technical faults, logos and duplicate segments before they reach the archive. A news operation might start with transcription and quality markers for selected field footage. Limiting the first stage makes it easier to test mappings, permissions, confidence thresholds and operator responses.

The integration team should document the asset lifecycle from creation to deletion. That documentation needs to show where the original media resides, how Galaxy identifies it, when ReCAP receives a processing request, where results are stored and what happens when an analysis fails. Retry handling, partial results and version changes deserve attention before the system is exposed to high-volume production.

A practical rollout can proceed through these stages:

Once the pilot is stable, the organisation can expand by content type, analysis service or location. New capabilities should be added through the same governance and measurement framework. That keeps the MAM useful to editors and archivists while allowing research-led video analysis to mature in a controlled production setting.

For the next implementation step, choose one Dalet Galaxy workflow—such as sports ingest quality control—and document its asset ID, trigger point, required ReCAP results, human review rule and success metric.