03 — Data Type
Video Annotated Data
Structured visual datasets for computer vision and video intelligence.
What it is
The hard part is not the drawing.
Video annotation is where most datasets quietly fail. A box drawn correctly on one frame and inconsistently on the next teaches a tracker the wrong thing.
The work is less about drawing and more about holding a definition steady — what counts as the object, where its boundary sits when it is partly hidden, and when a track should end rather than drift.
Those decisions are written down before annotation begins and checked across sequences, not just within single frames.
Why it matters for AI
Three things that decide whether it trains well.
- 01
Consistency beats precision
A slightly loose box applied the same way every time is more useful than a tight box whose definition drifts. Models learn the rule you actually followed, not the one you intended.
- 02
Occlusion is the hard case
Most disagreement in video annotation happens when an object is partly hidden. Deciding that once, in writing, is what keeps a track coherent across a sequence.
- 03
Identity has to survive the gap
When an object leaves frame and returns, whether it keeps its identity is a modelling decision. Made implicitly, it produces tracks that fragment for no reason the model can learn.
Applications
What this data trains.
Each of these draws on the category differently, which is why the specification is written per project rather than per service.
- Computer Vision
- Object Detection
- Object Tracking
- Activity Recognition
- Human-Object Interaction
- Video Intelligence
- Autonomous Systems
Collection possibilities
Where the data comes from.
Most engagements are a mix: annotate what you already hold, and collect specifically where coverage is thin.
Annotating footage you hold
The common case: existing video, annotated to a specification written with your team, with edge cases escalated rather than resolved silently by whoever is on the task.
Collection where coverage is thin
Where a class or condition is under-represented, footage can be captured specifically to fill that gap rather than waiting for it to appear in production data.
Label schema definition
If the schema does not exist yet, defining it is part of the work — a label set that cannot be applied consistently by two people is not finished.
Annotation possibilities
Scoped to the project, not sold as a bundle.
These are the operations available for this data type. Which of them a project uses depends on what the model has to learn.
- 01Bounding boxes
- 02Object tracking across frames
- 03Frame and clip classification
- 04Activity and event labelling
- 05Temporal segmentation
- 06Consistency review across sequences
Workflow
The same five stages, every project.
Understand
We start from the model and the use case, not the file format — what the system needs to learn, and what would make an example useless.
Specify
Requirements become a written collection or annotation guideline with worked examples and an acceptance definition, agreed before work starts.
Execute
Trained annotators and collectors work to that guideline, with edge cases escalated rather than guessed at.
Validate
Human review against the same rubric, plus consistency checks across annotators before anything is marked complete.
Deliver
Structured output in the schema your pipeline expects, with the guideline and review notes attached.
Quality workflow
Six gates between raw input and delivery.
The process does not change per data type. A corpus assembled across more than one category has to hold together, so the acceptance standard is shared.
Collection
Capture or sourcing against a defined specification, so the input is already in scope.
Guidelines
A written definition of correct, with worked examples and explicit edge-case handling.
Annotation
Trained annotators working to that guideline, escalating ambiguity rather than guessing.
QA
Structural and consistency checks across the batch, not just within single items.
Human Validation
Review by a second person against the acceptance definition.
Final Dataset
Structured delivery with the guideline and review notes attached.
Dataset preparation
What arrives at the end.
The output is agreed before work starts, so delivery is a handover rather than a translation exercise.
- Annotations in the format your training pipeline reads, agreed before work starts
- Track identity maintained across frames and across occlusion, per the agreed rule
- Frame and sequence indices preserved so annotations align to the source video
- The annotation guideline delivered with the data, including the occlusion and identity rules
FAQ
Video Annotated Data questions.
Bounding boxes, object tracking across frames, classification, activity labelling and temporal segmentation, scoped to the project requirement.
Other data types
- 01
Egocentric Video Data
Human-perspective video data for systems learning to understand activities, environments and interactions.
- 02
Audio Annotated Data
Structured audio datasets for speech, voice and audio intelligence.
- 04
Text Annotated Data
Structured language datasets for NLP, LLM and conversational AI.
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