Public technical boundary · version 1.0

Serious video systems make uncertainty inspectable.

This is the architecture a prospective operator can evaluate without exposing credentials, paid-provider controls, private corpus identities, participant details, storage locations, or internal network topology.

System model

Source identity in. Versioned boundaries out.

The assisted-pilot shape deliberately separates source qualification, model evidence, interval decoding, review, and output identity. Rally detection does not silently become automatic match or game splitting.

Contract direction

Small invariants are more useful than a large vague API.

Source timebase

Integer millisecond boundaries are expressed against the original recording timeline, not an unnamed proxy.

Per run

Ordered intervals

Every end follows its start; windows are source ordered and non-overlapping.

Core

Explicit identity

Schema revision and the actually served model release travel with an accepted pilot handoff.

Pilot requirement

No decorative confidence

No confidence field is published until its semantics and calibration form a real public contract.

Deliberate omission

Visible partiality

Ambiguous, unsupported, or partial output must remain visible rather than being reported as a silent success.

Fail loud

Evidence method

The result is never separated from the ruler.

Results on request

Evaluation results, including tolF1, are available on request with the applicable context attached.

Candidate vs served

A development candidate is not represented as the model serving a future pilot. The actual served release is identified per accepted run.

Locked vs selected

Locked operating points remain distinct from calibration selected on development evidence.

Scope stays attached

Aggregate and sport-specific interpretation travels with representation, imbalance, and panel-history notes.

Scorer semantics

Matching rules, tolerances, aggregation, operating points, and limitations accompany any disclosed result.

Fresh confirmation

Fresh recordings remain a gate before broader performance language.

Owned golden labels

Human boundaries become the ruler through a controlled chain.

The capture session—not an extracted clip—is the split unit because clips from one sitting share camera, venue, lighting, and players. Automated checks can find malformed, overlapping, or suspicious labels; they cannot certify that a human missed no rally.

Dronacharya evaluation uses owned, human-labelled rally boundaries with session-aware data roles. Exact training inventory, sport distribution, split roles, and quality-control findings are supplied only with a contextual evaluation brief; the public site does not turn dataset counts into a performance claim.

Plain-language scorer

What tolF1 means here.

Example human and predicted rally boundaries inside agreed tolerance windows A human rally interval and one predicted interval are shown on the same timeline. The predicted boundaries lie inside the agreed start and end tolerance windows. source time human prediction start window end window
Illustrative interval only. Each prediction can match at most one human rally, and a match must satisfy both agreed boundary tolerances.
  • A human marks each rally start and end against the source timeline.
  • The system predicts intervals on that same timeline.
  • Maximum-cardinality one-to-one matching prevents one prediction from claiming multiple human rallies.
  • A match must satisfy the agreed start and end tolerance windows.
  • F1 is calculated per recording and aggregated across the agreed panel so a long recording cannot silently dominate the result.

Evaluation results, including tolF1, are available on request. Exact tolerances, aggregation details, panel construction, operating points, and limitations are supplied with the figures.

Conformance is not model quality. A consistent scorer does not prove representative data, media rights, venue generalisation, or a production serving release. Those remain separate evidence questions.

Release map

What exists, what is assisted, and what is not public.

SurfaceStatusBoundary
Model method, capture, resources and evidencePublic betaInspectable pages and downloads
Rally-index example and JSON SchemaPreviewSynthetic direction, no public endpoint
Evaluation briefOn requestEmail KhelVision; no submission form on this site
Media intake and processingAssistedOnly after fit, rights, transfer, retention, and deletion are agreed
General API, callbacks, batch, live streamNot publicNo compatibility or service-level promise
Named candidate serving claimNot madeDevelopment candidate evidence remains separate from serving identity

Marketing site

Read the method. Request the context.

This marketing site serves static descriptions, synthetic examples and downloads. It accepts no recordings, account credentials or form submissions and starts no inference. Cloudflare processes ordinary request metadata to deliver and protect these pages. To request a contextual evaluation brief, email hello@khelvision.com; use the KhelVision app site for product access and account policies.

Failure semantics

No silent success.

  • A source that cannot be qualified does not quietly become a valid run.
  • A model candidate, deployment readiness signal, and actually served release are separate facts.
  • Missing or ambiguous intervals remain reviewable; absence is not rewritten as zero.
  • A partial output is not described as complete, and a liveness check is not treated as proof of an accepted media job.
  • Release receipts and public evidence state what they prove and what they do not.

Safe publication

Transparency does not require an infrastructure leak.

Public material intentionally excludes credentials, authenticated endpoints, private hostnames, provider and billing identifiers, corpus and participant locators, local storage paths, internal incident details, and commands that can mutate paid infrastructure. Those details are unnecessary to evaluate the public product and would weaken operational assurance.

What remains public is the useful layer: capture guidance, system boundaries, schema direction, synthetic examples, the evaluation-disclosure policy, scorer semantics, limitations, privacy and terms, and the public labeling contracts behind owned golden labels.