VIDEO DATA. BUILT IN THE EEA
A frame tells you where. A sequence tells you why.
We collect video against the task your model has to solve, with specified coverage, quality control and documented rights.
A model trained on frames learns where things are. The change between frames is where the decision lives, and it is what this collection is specified to capture.
The counter shows how many frames this step contains. Show dropped frames plays the step at 10 fps, the loss the platform checks for on every recording.
WHAT YPAI COLLECTS Programmes YPAI runs · Related pages
Start from the movement your model is missing
Eight programmes YPAI runs, and three adjacent kinds of work with their own pages. Each programme names the movement it captures; open it for the problem, the delivery and the annotation it hands off to.
- 01
Collection at scale
The same scene under every condition it will meet.
The problem and the delivery
The footage your model needs does not exist, or misses the conditions it will meet.
Scenario-based, quota-managed capture across countries, environments and devices, remote or moderated, with reshoot workflows and delivery manifests. A coverage gap becomes recording instructions.
Coverage is specified per cell before volume is counted.
- 02
In-cabin and dashcam
A glance away from the road is captured as a sequence of frames.
The problem and the delivery
Driver, occupant and road-facing models need European cabins and roads under real conditions.
Driver monitoring, occupant monitoring, cabin activity and road-facing video, captured across the drivers, vehicles, lighting and weather the deployment will meet. Voice in the cabin is its own programme.
Coverage of cabin and road that matches the deployment, with geographic scope defined against the intended use.
- 03
Human motion, gesture and pose
Each action is recorded from its start to its end.
The problem and the delivery
The model needs movement, interaction or several views of the same action.
Full-body and upper-body motion, gestures, locomotion and action sequences, with multi-view synchronisation where the task requires it. Pose and keypoint labels are annotation work and have their own page.
Synchronised human-action video collected for the temporal task.
- 04
Video integrity and synthetic media
Manipulation often shows between frames before it shows in a single frame.
The problem and the delivery
Detection models go stale as generation methods move, and benchmarks hide their failure modes.
Manipulated-media datasets, synthetic-media evaluation, authenticity review and human-perception studies, on provenance-aware footage. Handling and purpose limits are agreed per engagement.
Real footage with its record, so the authentic half of the benchmark is documented.
- 05
On-camera speech and conversational video
Lip movement and sound are checked for synchronisation.
The problem and the delivery
Visible speakers, audio-video sync and lip-sync-sensitive data, recorded as one programme.
Solo, paired and multi-speaker recording with isolated speaker tracks, transcription and diarisation, demographic and language quotas. Speaker recruitment and the consent record live on the speech hub.
Isolated per-speaker tracks and audio-video sync fixed in the specification.
- 06
Licensed video datasets
Every licensed clip is delivered with its provenance.
The problem and the delivery
The deadline does not allow custom collection, or a rights review failed on the set you have.
Existing inventory identified, sourced through partners, sampled and technically reviewed, rights checked, licensed, packaged and delivered. License what exists and collect only what is missing.
Chain of title and permitted use are checked before a sample is shared.
- 07
Indoor activity and consumer behaviour
A fall, a hand-over, a purchase: each is a change across frames.
The problem and the delivery
Home, workplace and in-store models need everyday activity under the conditions people actually live in.
Human activity, consumer behaviour and work-task video in indoor and outdoor environments, device and environment controlled, remote or moderated, with consent per contributor and purpose.
Footage from opt-in households and workplaces.
- 08
Face, identity and talking-head video
Expression, gaze and head pose are recorded as movement across frames.
The problem and the delivery
Facial movement, expression, gaze and head pose across identities, sessions and lighting.
Per-project consent and rights scope (model release, permitted use, reuse) shape how this delivery is set up, with file-to-participant traceability throughout.
Repeated-session identity and speech-linked facial movement, traceable to the participant file.
- 09
- 10
Egocentric and physical AI video
First-person video, hand-object interaction and task demonstrations for robot learning.
- 11
Video annotation and evaluation
Tracks, temporal events, action labels and keypoints on footage you already hold.
Also collected
- Indoor and outdoor scenario capture
- Multi-country, quota-managed cohorts
- Cross-device and cross-environment variability
- Avatar and animation motion data
- Gap-fill against footage you already own
Working with a scenario not listed here?
Describe the deployment and the condition it runs under, and YPAI designs the capture protocol.
THE CONDITION SPACE Camera · Environment · People · Motion
Four axes decide whether a sequence survives deployment
You name the range your deployment has to hold. The programme is built to span it, and every recording lands with its coverage in its record.
Camera
The mount, the sensor and the rate are fixed in the specification, because a model trained on one capture mode degrades on another.
Range specified
- Smartphone, fixed mount
- Smartphone, handheld
- Webcam
- In-cabin camera
- Frame rate and resolution selected for the task
Environment
The street, the cabin, the room. Light, weather and glass are specified, not hoped for.
Range specified
- Street at blue hour
- Automotive cabin and road
- Home
- Workplace and in-store
- Rain, snow, night
People
Quota-managed cohorts, identity-verified, with the rights for every visible person recorded.
Range specified
- Multi-country, quota-managed cohorts
- Age range, gender identity, skin tone
- Identity-verified contributors
- Facial and biometric rights recorded
Motion
The change between frames is the edge case. It is captured on purpose, at the rate the decision needs.
Range specified
- The same scene under every condition it will meet
- Fast motion, frame rate selected for it
- Occlusion mid-sequence
- Low light
- Dropped and duplicated frames
The reading in each tab is taken from the frame itself: sharpness as the variance of the Laplacian, and the share of pixels under 25 IRE. The platform takes the same readings on every recording it accepts.
Coverage is agreed before capture begins and delivered as part of the record. Where a required range cannot be covered, YPAI says so during scoping rather than after delivery.
HOW A PROGRAMME RUNS Specification · Capture · Quality gates · Delivery
One requirement becomes one accountable video operation
Model task, scenario, cohort, environment and acceptance criteria are written before capture. The same specification is what the footage is reviewed against, and what the delivery record documents.
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The specification is written first
The model task sets scenario, cohort, environments, capture mode and acceptance criteria, before anyone records.
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Footage recorded under the specified conditions
Identity-verified contributors record under the specified conditions; rate, resolution and sync are fixed in the specification.
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Each recording accepted, or returned with a reason
Automated checks, then human review against the specification; a rejected item returns with its reason.
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The footage delivered with its record
Accepted footage leaves with its metadata, rights and provenance record, acceptance report, checksums and manifest.
YPAI PROPRIETARY DATA COLLECTION TECHNOLOGY PLATFORM · RECORD · RIGHTS
We built the system behind the data.
YPAI runs its own data collection and assurance platform, built for GDPR obligations, across speech, video, image, text, human feedback, agents, robotics and other multimodal programmes.
- Identity
- Asset, modality and schema version
- Origin
- Source, device, application, environment and time of creation
- Contract
- The requirements it was collected against, and their coverage
- Privacy
- Processing purpose, legal state and data categories; consent per visible person
- Quality
- Rule-based, statistical, model-based and cross-modal checks, then a human verdict
- Agreements
- Applicable statements, their hashes and signatures
- Verdict per recording
- PASS
What the platform measures on every video recording
- Native resolution and frame rate
- Dropped and duplicated frames, bitrate and codec
- Focus, exposure, motion artefacts and continuity
- Audio-video synchronisation
- Participant visibility, scene compliance and liveness
- Camera provenance, facial and biometric rights
Specification Your specification and the signed agreements become the project the platform runs against
The record your legal and security review will ask for
The operation produces the governance artefacts as it runs. Depth and format follow your risk profile and are agreed during scoping.
What is the legal basis for each recording?
Consent, legitimate interest or contractual necessity, documented per recording
What may the footage be used for, and by whom?
Permitted use, release and licence scope defined per engagement; reuse by explicit written grant
Where did each recording come from?
Provenance per recording, capture metadata and checksum, lineage from source to derivative
Where does the footage reside, and under whose jurisdiction?
EEA infrastructure by default, Norwegian jurisdiction; EEA-only sourcing on request
What happens to the footage when the engagement ends?
Erasure within 30 days of contract end; retention and closeout defined in the engagement
What governs the engagement on paper?
DPA signed, SCCs available for cross-border transfer
Read the technical brief Request a sample assurance record →
START WITH THE SPECIFICATION Describe the system · Feasibility read · Pilot · Production
Bring the footage problem. Leave with a capture plan and an acceptance proposal.
- 01 Describe the system Model task, target scenario, cohort or geography, scale and timeline.
- 02 Feasibility read Capture plan, coverage matrix, rights scope and acceptance proposal.
- 03 Pilot A scoped delivery against the agreed specification, reviewed before production.
- 04 Production Full programme with ongoing review, versioned delivery and support.
specified · reviewed · delivered with its record
We will define the video programme required for your deployment context and constraints.
If YPAI is not the right fit, we will say so directly.