How to Choose an Annotation Partner for Egocentric Video
Use a representative trial to see how a partner handles ambiguity.
Two annotators watch the same clip of someone picking up a mug. One marks the grasp when the fingers first touch it. The other marks it when the mug leaves the table. Both answers can feel reasonable until the project defines which event it needs.
That small disagreement captures a large part of choosing an annotation partner for egocentric video. You are choosing a team that must interpret first-person actions consistently, explain uncertainty and deliver labels your pipeline can use. A low price per frame tells you very little about those abilities on its own.
Start with a label brief
Describe the intended training or evaluation use and the evidence available in the footage. Then specify the labels required: action intervals, object identities, hand contact, keypoints, outcomes or another agreed layer.
For each layer, define the unit of work. Is the vendor labeling frames, episodes or continuous video? Does the price include tracking across time, review, adjudication and export? How are empty frames, occlusion and unusable footage handled?
Keep ambition connected to visibility. A partner should be able to tell you when the recording does not support the requested label, rather than supplying a confident-looking guess.
Test the difficult parts of your actual footage
Use a trial drawn from representative sessions, including cases likely to cause disagreement: overlapping hands, tool use, repeated actions, long pauses and objects that leave the view.
Give candidates the same instructions and enough context to interpret the task. Include ordinary easy cases as well as edge cases. A trial made entirely of unusual failures will not describe the production workload either.
AWS’s guidance on labeling instructions recommends testing instructions on examples from your own data and refining them before a larger job. Apply that principle to the vendor trial: review where the instructions were unclear, then check whether the team incorporates the correction.
Ask how quality is measured
“High accuracy” needs a definition. Ask which metric applies to each label layer, how samples are selected and who resolves disputed cases.
For temporal labels, you might measure boundary error against an adjudicated reference with an agreed tolerance. For object classes, examine per-class performance rather than only a pooled score. For keypoints, define coordinate conventions, visibility states and the treatment of points that cannot be observed.
Inter-annotator agreement measures consistency between annotators. It is useful, but agreement alone does not establish correctness: two people can follow the same mistaken rule. Review agreement alongside performance against a trusted reference and a process for updating that reference when a genuine ambiguity appears.
Look at the review process, not just the first pass
Ask the partner to walk through one difficult example. Who labeled it, who reviewed it, what changed and how did the decision reach the rest of the team?
A useful workflow includes a route for questions, an owner for the taxonomy and a record of guideline changes. Ask how new annotators are trained and how the team checks for drift across later batches.
You do not need every internal personnel detail. You do need confidence that the production process resembles the process used for the trial, with clear ownership when quality falls below the agreed threshold.
Check the export early
Request a sample in the format your pipeline will ingest. Validate identifiers, timestamps, units, label names and schema versions. Confirm how labels point back to the source session and the exact video used for annotation.
This matters when clips have been trimmed, transcoded or anonymized. A correct label attached to the wrong time base is still a failed delivery. Decide how revisions are issued so an updated batch cannot quietly mix with an earlier version.
Also agree access controls, permitted subcontracting, data locations, retention and deletion evidence. These are project requirements to settle, not assumptions to infer from a security badge on a website.
Compare the full cost of accepted labels
Ask quotes to separate setup, annotation, review, rework and changes in scope. Check what happens when the footage is harder than the trial or the taxonomy changes after production begins.
Record how much time your own team spends resolving issues. A cheaper annotation quote can require more internal review; a higher quote may or may not include that work. Compare the total process rather than assuming either price signals quality.
Agree the trial’s success criteria before it starts. Include label quality, export compatibility, question handling and delivery reliability. Keep commercial terms explicit so the trial itself does not become a source of confusion.
Choose a partner you can learn with
An early batch should improve the project’s understanding of its data. The partner’s questions can be as informative as its scores, especially when they reveal an event boundary or visibility rule nobody had defined.
Guardian provides egocentric data gathering and annotation, including task segmentation, hand-object interaction and outcome labels. Bring representative clips and the intended model use to the scoping conversation so the pilot can test the labels that matter to your project.
