julian
8a8e74561d
Pivot from the hardware/integrity layer to the parking operation. All wiki-only; no code yet. Core principle throughout: business entities are projections over the signed append-only event log, never mutable tables. New concepts: parking-session, tariff (composable/versioned, FX-ready), shift (manned-only Z-report), capacity-occupancy, validation-discounts, reporting-analytics, clock-integrity, ticket-encoding, anti-passback. New entities: permit, opencv-anpr-service, blocklist. Decisions: session-model, vision-service (host-side ANPR + vehicle verification; scoped AGPL exception for the isolated service). Updates: append-only-event-chain (new event types + vision witness), local-jwt-auth (drop 8h expiry -> until logout; code change pending), lpr-camera (host-side recognition supersedes edge-AI), standing-decisions (AGPL exception), open-questions (+FX, +pay-station money corners, backup). Deferred + flagged: intercom/help-call, receipts/refunds/change, FX engine, lane topology (#1).
62 lines
3.4 KiB
Markdown
62 lines
3.4 KiB
Markdown
---
|
|
type: decision
|
|
tags: [parking, decisions, vision, anpr, anti-fraud]
|
|
sources: []
|
|
updated: 2026-06-15
|
|
status: open
|
|
---
|
|
|
|
# Decision: Host-side Vision Service (ANPR + vehicle verification)
|
|
|
|
Taken 2026-06-15, as part of the business-layer build ([[session-model]]).
|
|
|
|
## Decisions
|
|
|
|
1. **Build a host-side vision service** ([[opencv-anpr-service]]) that does ANPR (plate → identity)
|
|
**and** vehicle-attribute verification (anti-spoofing witness) on snapshots from ordinary
|
|
Hikvision/Dahua cameras.
|
|
2. **It replaces the dedicated edge-AI [[lpr-camera]]** as the recognition path: ordinary IP cam →
|
|
snapshot (`Snapshot.bytes`, already pulled by the camera driver) → vision service → plate +
|
|
vehicle. Removes the special LPR camera from the [[bom]] as a requirement (still allowed as an
|
|
option).
|
|
3. **Deployment: a separate local Python/OpenCV microservice** on the appliance, called over
|
|
**localhost HTTP** by the Node backend. Fully offline ([[offline-first]]); its own process and
|
|
failure domain; the host falls back to the ticket path if it's unavailable.
|
|
4. **Licensing exception:** AGPL components (e.g. YOLO plate/vehicle models, OpenALPR) are
|
|
**permitted inside this service only**, because it's a separate process not linked into the app —
|
|
the app stays strictly MIT/Apache/BSD. Amends [[standing-decisions]].
|
|
5. **Recognition is advisory, evidence is authoritative.** A read never single-handedly authorizes
|
|
a paid/access barrier open; it flags for [[reconciliation]] and attaches (with the source image)
|
|
to the signed [[append-only-event-chain]] entry. Low confidence → fallback, never strand a car
|
|
([[fail-state-safety]]).
|
|
|
|
## Why
|
|
|
|
- **Replace vs. edge-AI camera:** host-side recognition on cheap IP cams shifts cost from per-lane
|
|
smart cameras to one compute box + our software; gives us the raw image for the second job below.
|
|
- **Vehicle verification is the real prize (user-driven, 2026-06-15):** plate-only ANPR can't catch
|
|
a **printed/spoofed plate on a different car**. Extracting vehicle attributes/fingerprint lets the
|
|
system reconcile *the car*, not just the number — directly filling the independent-witness gap the
|
|
[[append-only-event-chain]] calls out as unbuilt.
|
|
- **Separate-process + AGPL-scoped** keeps the app's permissive-license guarantee intact while not
|
|
crippling accuracy (the strict permissive-only ANPR path is markedly weaker — that tradeoff was
|
|
weighed and the scoped exception chosen).
|
|
|
|
## Rejected / alternatives
|
|
|
|
- **Strict permissive-only ANPR in-app** — license-clean but weaker accuracy and more build; the
|
|
separate-process AGPL exception was chosen instead.
|
|
- **Keep the edge-AI LPR camera as primary** — viable fallback if host-side accuracy disappoints;
|
|
not chosen now, kept on the table in [[opencv-anpr-service]].
|
|
- **Embed OpenCV in Node** (opencv4nodejs/WASM) — rejected: native-build pain, weaker model
|
|
ecosystem, no process isolation, shares the app's failure + license surface.
|
|
|
|
## Open / next
|
|
|
|
- Recognizer + vehicle-model selection and accuracy targets; fingerprint method + anomaly
|
|
threshold ([[opencv-anpr-service]]).
|
|
- Appliance compute footprint (CPU vs. small GPU/NPU) — [[bom]] / [[open-questions]].
|
|
- Service API + the Node-side adapter; per-camera opt-in wiring.
|
|
- Reconciliation logic that consumes plate+vehicle witness vs. commanded opens (still unbuilt — see
|
|
[[append-only-event-chain]], [[reconciliation]]).
|