AVIS — Demo Video Script (~3.5 min)
A tight, honest walkthrough. The thesis: most teams claim all 7 violations from one photo; a real camera can't, and neither can they — AVIS is the system that knows the difference.
Pre-flight checklist (do this before recording)
-
.envset:HELMET_WEIGHTS=models/helmet/best.pt,PLATE_PROVIDER=fastalpr,LLM_PROVIDER=gemini,GEMINI_MODEL=gemini-2.5-flash. (Seatbelt optional:SEATBELT_CHECK=true.) -
cd frontend && npm run build(so FastAPI serves the React app at/). - Start:
venv\Scripts\python.exe -m uvicorn api.main:app --reload→ open http://127.0.0.1:8000 - Have 3 images ready: (A) a clear motorcycle with a no-helmet rider (+plate visible), (B) a clean street photo with no violations, (C) a dark/blurry photo.
- Quota note: helmet + plates are free/local. Triple-riding & seatbelt may make ~1 Gemini call each — fine for a demo; just don't spam re-uploads.
- Optional: pre-run a couple uploads so Analytics has data to show.
Scene 1 — Hook (0:00–0:25)
On screen: title card → the Violation Detectability Matrix (DESIGN.md §3).
"Every hackathon traffic project promises to catch all seven violations from a single photo. Here's the uncomfortable truth: you physically can't prove wrong-side driving or a red-light run from one still frame — and a real enforcement camera doesn't either. So we built AVIS: a system that detects what it can prove, and is honest about what it can't."
Scene 2 — The idea (0:25–1:00)
On screen: the architecture diagram (PROJECT_STATUS.md §2).
"AVIS is a hybrid pipeline. Deterministic computer vision does the detecting and measuring. Detections become an Evidence Graph — riders linked to motorcycles, drivers to cars, plates to vehicles. Pure rules read that graph and propose violations. Each gets a tier: Tier A is provable from appearance, like no-helmet; Tier C needs camera calibration; Tier D needs video. A vision-language model is used only as an auditor to verify ambiguous cases — never as the detector — and it's allowed to say 'insufficient evidence'. Only Tier A can auto-confirm. Everything else is routed to AI verification or a human."
Scene 3 — Live demo (1:00–2:40)
3a. A real violation (1:00–1:50)
Action: Analyze tab → upload image A (camera_id = cam_demo) → wait for cards.
"I'll upload a real street photo. No model training, no cloud GPU."
Point at the result, then open a violation card:
"It detected the motorcycle and rider, flagged No Helmet, and — this is the key part — it explains itself. Here's the confidence breakdown: detection, rule, and attribute scores fused into 0.9-something, above the auto-confirm threshold. The helmet read came from a local model — zero API calls. It cites the law: Motor Vehicles Act §194D, ₹1000. The plate was read by an on-device OCR. And there's a tamper-evident SHA-256 evidence hash and a draft e-challan. That's a court-ready evidence package, not a black-box verdict."
3b. Abstention is a feature (1:50–2:15)
Action: upload image C (dark/blurry).
"Now a degraded photo. Instead of guessing, the quality gate makes the system abstain — 'I can't judge this fairly.' Abstaining beats a false accusation. That honesty is the whole point."
3c. The dashboard (2:15–2:40)
Action: Analytics tab → then Review queue → then Search.
"Analytics shows violations by type, by outcome, and a human-review-reduction number — the share resolved automatically. The review queue is the human-in-the-loop: approve or reject with a note that's written to the audit trail. And every record is searchable by type, status, or plate."
Scene 4 — Proof & honesty (2:40–3:05)
On screen: terminal with pytest -q (49 passed) and the eval harness file.
"Under the hood: 49 tests, clean lint and types. The evaluation harness reports per-violation precision/recall/F1, a rule-only-vs-rule-plus-VLM ablation, plate accuracy, and latency — and it reports Tier A separately, so we never average a hard number into an easy one. We don't show fabricated accuracy; drop in a labelled set and the numbers are real."
(If you have a labelled data/eval/ set: show python -m eval.run output here instead.)
Scene 5 — Close (3:05–3:30)
On screen: the e-challan card + "free / self-hostable" line.
"Everything runs on free, self-hostable components — one
docker compose upbrings up the API, worker, Postgres, Redis, and object storage. The VLM is the only external piece and it's on a free tier, behind a provider-agnostic client — swap in a paid model later without touching the pipeline. AVIS: it detects what it can prove, audits what's ambiguous, abstains on the rest, and explains every decision. Thanks for watching."
Backup / B-roll
- Pre-recorded successful upload (in case of live network/quota hiccups).
- Screenshot of a violation detail + e-challan.
- The matrix and architecture diagrams as static slides.