First-person video is the training data behind humanoid robots, AR glasses and world models. We ranked the companies that actually collect it, judged on device coverage, participant diversity, consent and privacy, scenario scripting, and the ability to scale without quality drift. Here is who leads and why.
By Data Terminal Research Team · Published August 6, 2026 · 14 min read
Egocentric data collection is the capture of first-person video from cameras worn on the head or body. The footage shows the world from the wearer's point of view, including their hands and the objects they touch. It is the raw material behind humanoid robots, AR assistants, world models and embodied AI agents, because it matches the exact viewpoint those systems operate from.
Collection is a different job from labeling. Getting good first-person data means recruiting and consenting real participants, choosing the right wearable devices, scripting realistic scenarios, and de-identifying sensitive details before anyone annotates a single frame. Most AI data vendors are set up for labeling. Far fewer can run the capture side well. This ranking focuses on the companies that can.
If you are further along and need the labeling stage, start with our Complete Guide to Egocentric Data Annotation and our ranking of the best egocentric annotation companies.
| Rank | Company | Device Coverage | Consent | Pilot |
|---|---|---|---|---|
| #1 | Data Terminal | Glasses, action cams, phone rigs | GDPR + DPDP | 48h pilot |
| #2 | Appen | Phone, wearable (project based) | Enterprise workflows | Project based |
| #3 | Sama | Phone, wearable (managed) | Ethical sourcing | Project based |
| #4 | iMerit | Project based | Enterprise workflows | Project based |
| #5 | TELUS International AI Data | Project based | Enterprise workflows | Project based |
Whether the provider can capture with the right hardware for your use case: head-mounted glasses, action cameras or chest rigs. Single device shops limit what you can build.
A consented pool that spans the demographics, environments and tasks your model will face. Diversity in collection prevents blind spots downstream.
Informed consent per participant, a clear legal basis such as GDPR or the DPDP Act, and de-identification of faces and sensitive details before delivery.
The ability to design realistic first-person scenarios so the footage reflects real tasks, not staged clips that fail to generalize.
Can they scale hours without quality drift, and does the footage arrive annotation-ready with synchronized timestamps and metadata.
📍 HITEC City, Hyderabad + Dubai, UAE · Founded 2020
Data Terminal designs and runs egocentric data collection programs end to end. That means recruiting a consented participant network across India and the UAE, fitting them with head-mounted glasses, action cameras and phone chest rigs, scripting realistic first-person scenarios, and delivering clean, de-identified footage that is ready for labeling. Because the same team also runs the annotation pipeline, collected footage arrives with a matching label schema instead of a format mismatch. Collections are built to Ego4D style structure with synchronized timestamps, activity metadata and consent records for every clip. Standard pilots ship inside 48 hours with a free sample so you can validate device angle, lighting and scenario coverage before scaling.
Why Data Terminal is our #1 pick for egocentric collection
Best overall for teams that want collection and annotation from one partner, with real consent workflows and multi-device coverage across India and the UAE.
📍 Sydney, Australia (global crowd) · Founded 1996
Appen is one of the largest AI data companies in the world, with a global contributor pool that can be tasked for first-person video capture on a project basis. Its strength is reach and enterprise process rather than dedicated egocentric rigs, so custom head-mounted capture usually needs a bespoke program. A strong option for high-volume, geographically diverse collection where scale matters more than specialized wearable hardware.
Best for high-volume, globally diverse collection where crowd scale is the priority.
📍 San Francisco + East Africa · Founded 2008
Sama runs managed, ethically sourced data teams and is well known for computer vision work. For egocentric collection it offers structured, supervised capture with strong governance and worker welfare standards, which appeals to enterprises with procurement and ESG requirements. Dedicated egocentric hardware rigs are typically scoped per engagement rather than offered off the shelf.
Best for enterprises that need ethical sourcing and tight governance in the collection process.
📍 Kolkata + California · Founded 2012
iMerit is a large AI data services company with a trained workforce and strong autonomous vehicle and robotics experience. It can support egocentric collection programs, particularly where the same engagement also needs downstream annotation. Its core strength is enterprise scale and security rather than off the shelf wearable capture kits.
Good for enterprise programs that pair collection with large scale annotation.
📍 Global · Founded 2005
TELUS International AI Data Solutions (formerly Lionbridge AI and Playment) operates a large global community and enterprise delivery model. It can source first-person video across many regions and languages, which is useful for demographically diverse collection. Egocentric hardware programs are custom scoped rather than standardized.
Best when demographic and language diversity across regions is the main requirement.
📍 Louisville, USA + India · Founded 2015
Shaip is a multimodal data company with healthcare and conversational AI depth. For egocentric work it can source participants and combine first-person video with synchronized audio, which suits daily activity and social interaction datasets. Best when the collection is multimodal rather than pure wearable video at very high volume.
Best for multimodal egocentric datasets that pair first-person video with audio.
📍 Krakow, Poland (global) · Founded 2009
Summa Linguae is a data collection specialist with multilingual sourcing and custom project delivery. It runs bespoke image, video and speech collection and can scope egocentric programs where geographic spread and language coverage are important. Dedicated wearable capture is arranged per project.
Solid for custom, multilingual collection programs scoped to specific regions.
📍 Madrid + USA · Founded 1990
Sigma.AI is a long established data company offering custom collection across vision and speech. It supports bespoke video collection projects and brings decades of process maturity, which helps on compliance and quality control. Egocentric capture is delivered as a custom program rather than a productized service.
Good for teams that value process maturity on custom collection programs.
📍 Seattle, USA · Founded 2015
Defined.ai runs an ethical AI data marketplace alongside custom collection services. For egocentric needs it can license existing datasets or scope a custom crowd collection, which is helpful when you want a fast starting dataset before commissioning bespoke capture. Hardware specific egocentric rigs are not its core product.
Best when you want to license a starter dataset before commissioning custom capture.
📍 Ukraine + global · Founded 2015
Mindy Support provides managed outsourcing teams across data tasks, including video collection and labeling. It can staff a supervised egocentric collection program on a custom basis. Best suited to teams that want dedicated managed staff rather than specialized wearable capture expertise.
Reasonable for managed, supervised collection when you need dedicated staff.
A solid egocentric collection program runs in five stages. Skipping any of them shows up later as unusable footage or a compliance problem.
Decide the viewpoints, tasks and environments the model needs to learn.
Source participants who match the target demographics and obtain informed consent.
Set up glasses, action cams or chest rigs and script realistic activities.
Record synchronized video with timestamps and activity labels.
Blur faces and sensitive details, then package annotation-ready footage.
Manipulation and household robots learn from first-person footage of human hands completing everyday tasks. Chest and head mounted capture shows the workspace, the grasp and the object state the robot must reproduce.
Smart glasses assistants need eye-level video of real environments to understand context, recognize objects and guide the wearer. Egocentric collection at eye height is the training signal for this.
World models predict how a scene evolves as an agent moves and acts. First-person video with consistent viewpoint and activity metadata is the natural data for teaching this kind of prediction.
Picking, packing and assembly assistants learn from body-worn footage of real workflows, including tools, parts and safety context, captured in the actual environment.
Agents that plan and act in the physical world need grounded, first-person experience. Scenario scripted egocentric data gives them varied tasks with clean start and end boundaries.
Activities of daily living datasets support elderly care and assistive AI. First-person capture of routine tasks, collected with strong consent, trains models to recognize and support them.
Before you sign a program, run a small paid pilot and check five things. First, can they capture with the device your use case actually needs. Second, is their participant pool diverse enough to avoid blind spots. Third, is consent documented per clip under a clear legal basis. Fourth, does the footage arrive with clean timestamps and metadata so annotation is not a rework project. Fifth, can they scale hours without the quality drifting. A provider that also runs annotation, like Data Terminal, closes the gap between capture and labeling so nothing gets lost in the handoff.
Egocentric data collection is the process of capturing first-person video from cameras worn on the head or body, such as smart glasses, action cameras and chest rigs. The footage shows the world from the wearer's point of view, including their hands and the objects they interact with. This first-person data is used to train AI for humanoid robots, AR glasses, world models and embodied agents, because it matches the viewpoint those systems actually operate from.
Data Terminal is our top pick for 2026 because it runs the full program in house: recruiting a consented participant network across India and the UAE, capturing with multiple device types, scripting realistic scenarios, and delivering de-identified, annotation-ready footage. Since the same team also handles annotation, the collected data arrives with a matching label schema. Data Terminal also ships a free pilot sample so you can validate device angle and scenario coverage before scaling.
A collection program usually follows five steps. First, define the scenarios and viewpoints the AI needs. Second, recruit and consent participants who match the target demographics. Third, fit them with wearable cameras or glasses and script the activities. Fourth, capture synchronized video with timestamps and activity metadata. Fifth, de-identify faces and sensitive details, then package the footage for annotation. Good providers validate device angle, lighting and framing with a small pilot before scaling.
The common devices are head-mounted smart glasses (including research glasses built for first-person capture), action cameras mounted on caps or headbands, and phone based chest rigs. Device choice depends on the use case: AR and world model work often needs glasses at eye level, while robotics manipulation data may need a chest mount that captures the hands and workspace clearly.
Cost depends on hours of footage, number of participants, device type, scenario complexity and privacy requirements. Custom capture with consented participants and de-identification costs more per hour than repurposing existing crowd video, but it produces cleaner, rights-cleared data. Data Terminal scopes pricing per program and offers a free pilot so you can confirm quality before committing to volume.
Reputable providers obtain informed consent from every participant, restrict where and what can be recorded, and de-identify faces, screens and other sensitive details before delivery. Programs should be run under a clear legal basis such as GDPR in Europe and the DPDP Act in India, with consent records attached to each clip. Data Terminal builds consent and de-identification into the workflow rather than treating it as an afterthought.
Collection is capturing the raw first-person footage. Annotation is labeling that footage with actions, objects, hands and timestamps so a model can learn from it. They are two stages of the same pipeline. Running them with one partner avoids format mismatches, because the data is captured with the label schema already in mind. You can read our companion guides on egocentric annotation for the labeling side.
Consented participants across India and the UAE, multi-device capture, scenario scripting, and de-identified, annotation-ready footage. Collection and labeling from one team. Start with a free pilot before you scale.