Egocentric Tire and Wheel Service Video Data

Egocentric tire and wheel service video data is real-world, first-person video of a person servicing tires and wheels: taking a wheel off, dismounting the tire on a machine, mounting and seating a new one, balancing the wheel, and rotating wheels between positions. It is recorded from the operator viewpoint, with depth, 3D hand pose, contact events and frame-accurate action segments aligned on one hardware clock.

Tire and wheel service asks a lot of a manipulation policy because it is a chain of heavy, awkward, multi-step handling, not a single grasp. A person lifts a wheel, positions it on a machine, breaks a bead, lubricates, mounts, seats, then moves on to balancing or refitting. A first-person view keeps the wheel, both hands and the machine in the same frame, which is the viewpoint a robot arm with a head or wrist camera would act from.

This page treats tire and wheel service as a task family in its own right. The automotive service page covers the broader bay, the fastening page covers driving and torquing fasteners, and the tool-use page covers hand tools in general. Here the task centres on the tire and the wheel itself.

The site states tire and wheel service in its task catalog. An environment called Auto Service Center & Tire Shop lists tire dismount and mount, dismount-mount-and-balance, wheel balancing, tire rotation with a pressure check, and wheel and tire dressing. Its listed examples include tire rotation and brake work next to oil changes and vehicle diagnostics.

Every one of those rows is recorded with the same capture mode, a head-mounted phone or a multiview rig. Skill ratings run from Easy for dressing to Medium for the tire and balancing rows, and statuses run from Active collection to Ready to deploy. Brake rows in the same environment also include removing the wheel, so wheel removal appears inside them as a step.

A custom spec is scoped from that vocabulary. You describe the wheels, machines, stations and sequence your policy needs, and the written spec covers them, with the catalog rows as a starting point rather than a fixed menu. The tables below list what the site states and the rows with their skill rating and status.

Two catalog rows describe the core tire change. One is described as breaking the bead, dismounting the tire on the machine, lubricating the bead and mounting the new tire. The other is described as removing the wheel, dismounting the tire on the machine and mounting and seating a new one. Both are rated Medium.

The first of them is Active collection, so recording is running now and a spec can join an existing capture stream. The second is Ready to deploy, so contributors and site access are set up and capture starts once a spec is signed. A third row dismounts, mounts and balances tires with a tire machine and a wheel balancer, also Medium and Active collection.

The site gives no tire size, no wheel type, no vehicle type and no number of wheels per episode for these rows. Which tires and wheels are used, how an episode starts and ends, and whether it covers one wheel or a full set are agreed in the written spec rather than assumed.

The Wheel Balancing row is described as mounting the wheel on the balancer, spinning it, reading the result and fitting weights to the rim. It is rated Medium and its status is Ready to deploy, so capture starts once a spec is signed.

The motion is a short chain with a clear order: lift and mount, start the spin, read, then fit. Action segments are useful here because the boundaries between mounting, reading and fitting are exactly the moments a policy has to sequence correctly. A spec can ask for the whole chain or isolate one step.

The site does not state balancer readings, weight sizes or tolerance values as delivered fields. If your policy needs the reading on the display captured as a label, that is defined in the spec as annotation depth, and the quote covers it before capture begins.

The Tire Rotation & Pressure Check row is described as swapping wheels between positions, checking tire pressure with a gauge and adjusting to spec. It is rated Medium and its status is Ready to deploy.

Rotation adds a different kind of movement to the machine work. It involves carrying and positioning wheels, handling the vehicle side of the job and reading a gauge. A head-mounted view keeps the gauge, the valve and both hands in frame, and a wrist-mounted camera is available for close-range detail.

The site does not state a pressure value, a gauge type or a vehicle type, and it does not state a measured reading as a standard label. Those are agreed in the written spec.

The site does not list lug-nut loosening or tightening as its own catalog row. Wheel removal appears as a step inside brake rows in the same environment, for example one described as jacking the car, removing the wheel, compressing the caliper and replacing the pads, which also names a lug wrench. Another is described as removing, replacing and torquing brake pads and rotors with a socket set and a torque wrench.

Those rows are rated Hard and are Active collection or Ready to deploy. They are brake work rather than tire work, so this page uses them as neighbours, not as tire rows. The existing automotive dataset page also lists torque wrench to spec on a wheel among its skills.

The site states no torque values, no lug-nut counts and no tightening sequence for any row. A spec that needs a lug pattern or a torque step captured defines it up front, and the fastening page covers the general hand pose and contact events behind driving and tightening fasteners.

Wheel and tire dressing is a lighter task: cleaning wheels and tires and applying dressing to the rubber and wheel faces. It is rated Easy and is Ready to deploy in the auto service environment. A matching row in a car wash and detailing environment is Active collection.

It is a useful counterpart to the machine rows because the hands do surface work, not heavy handling, and the wheel stays in place. A spec that needs a spread of difficulty can mix Easy dressing with Medium machine work.

The site names no product, polish or brand for dressing, and none is named here.

The catalog places the tire rows in its Auto Service Center & Tire Shop environment, with dressing also in a car wash and detailing environment. The site separately describes its automotive dataset as captured in working service bays rather than a staged mock-up, with a tyre bay among the listed bays next to a lift bay, engine bay work and under-vehicle work on a lift.

The site defines the statuses plainly. Ready to deploy means contributors and site access are set up and capture starts once a spec is signed. Active collection means recording is running now, so a spec can join an existing capture stream. Available on request means the work is scoped and recruited to spec when a buyer asks for it. The tire rows sit in the first two, so no tire row is currently scoped-to-spec only.

The site names no shop, dealership, vehicle or brand for any row, and it does not state capture at a customer own facility. Environment, access and schedule are agreed in the written spec.

The catalog names a head-mounted phone or multiview rig as the capture mode for every tire row. A head-mounted phone records the first-person view of real work on site, and a multiview rig adds synchronized third-person angles when the spec asks for them.

A head-mounted camera keeps the lift, position and operate motion in frame from the viewpoint a manipulation policy will see from, with a wrist-mounted camera available alongside it for close-range detail. Depth, 3D hand pose, contact events and action segments are aligned to video on one hardware clock, synced under 2 ms. Distance-invalid pixels beyond the depth sensor reliable range are flagged rather than interpolated.

The site does not state a camera count, resolution or frame rate specific to tire service, so those are agreed in the written spec rather than assumed. It also states that the vehicle itself can occlude the view in a service bay, so the second angle from a multiview rig is worth asking for.

Each episode follows the same episode schema used across Firsthand custom collections: ten streams synced under 2 ms, with head and wrist RGB, depth, 3D hand pose with per-joint visibility, contact and grasp events, and frame-accurate action segments. Instance segmentation is also stated for the automotive collection.

For tire and wheel service, hand pose tracks the operator through the grip, the lift and the fit, contact events mark when the fingers make and break contact with a wheel, a tire or a tool, and action segments mark the start and end of each step. Because the labels share one clock with the video and depth, a step boundary lines up with the exact frame where it happens.

Object labels use the terms your spec sets. The skill spec decides which object classes are in scope before capture, so labels follow the vocabulary your training stack expects rather than a generic taxonomy.

Torque values, lug-nut counts, vehicle or tire types, lift or tool specifications, throughput, force or tactile data, a task-completion label and any safety certification are not stated anywhere on the site as standard fields. A standalone lug-nut loosening row is not stated either.

If your policy needs any of these, they are defined in the written spec as annotation depth, and the quote covers them before capture begins. The page does not promise them by default, and it makes no safety or certification claim.

The site states that the automotive dataset holds a large share of cluttered and low-light work and that under-vehicle capture is hostile to infrared depth, because painted and oily surfaces absorb the projector pattern. Those episodes carry a depth-quality flag instead of being silently interpolated.

That applies to the same family of work this page covers. Wheel and tire handling near a vehicle puts surfaces in frame that depth sensors find hard, so a spec can ask for the depth-quality flag to be delivered with each episode and for the second angle to cover moments when the vehicle blocks the view.

The site does not state a depth accuracy figure specific to tire service. Any such requirement is defined in the spec.

Delivery formats are RLDS, LeRobot and WebDataset, plus HDF5, zarr and Rerun .rrd. The first three are the training-loader formats; HDF5 and zarr carry arrays for custom pipelines, and Rerun .rrd gives a multi-stream synchronized playback file for review. The format table below sets each against what its official documentation states.

Converter source is readable, so you can retarget the schema to your own loader, and the episode schema page lists the fields. Tire and wheel episodes use the same schema as every other Firsthand collection, so a loader that already reads another Firsthand dataset reads this one without changes.

Every batch is validated against signed accept criteria before it ships, with a reject log, and delivery is rolling, per validated batch.

Every episode links to a signed contributor release through a documented consent chain, and the consent record is part of the delivery. Contributors are the people who service the tires and wheels, so their release covers the footage captured of their hands and workspace.

Faces, plates and screens are detected and irreversibly blurred at ingest, and a reviewer confirms the result before anything ships. The site states that licence plates and VIN plates in service-bay footage are blurred at ingest without exception, and the anonymization record is delivered with the data.

Text on a tire sidewall or a wheel is not listed as a blur category on the site, so this page does not claim it is blurred. If that text matters for your use, raise it in the spec so the handling is agreed before capture.

Where coverage is already deep, the first validated batch lands in a median of 48 hours from signed spec. Thin environments typically take three to four weeks, because vetting operators there is the bottleneck, and we quote that timeline up front. The catalog status on each task row tells you which case you are in, and the tire rows are currently Active collection or Ready to deploy.

On-demand top-ups run against the same spec and accept criteria after each training run, so new tire and wheel data stays consistent with what you already hold. The site does not state a batch size or a later-batch turnaround, so those are quoted against your spec.

You describe the wheels, machines, stations and sequence your policy needs. We turn that into a written spec covering tasks, environments, capture tier, annotation depth and accept criteria, then quote reach, timeline and price against that document before any capture begins.

A Spec Pilot, a small and fast first batch, can prove the spec and the accept criteria before you scale volume. The collection runs through the same seven-stage end-to-end custom collection pipeline as every Firsthand program: contributor recruiting, capture, per-batch QA, anonymization and licensed delivery. Billing is per validated hour, a reject log ships with every batch, and rejected items are recaptured at our cost.

Every custom collection ships under a perpetual, buyer-owned commercial license, and exclusivity is available so the data is never resold. The license is documented alongside the consent chain, so a policy trained on the data can go into a product.