# HackPulse

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Shree sowbika SY](https://cerebralvalley.ai/u/shreesowbika), [Srishanth S](https://cerebralvalley.ai/u/srishanth), [Raj Kumar](https://cerebralvalley.ai/u/Rajkumar_2006), [Suman Maitreya](https://cerebralvalley.ai/u/Tatasumu)
- **GitHub:** https://github.com/Suman-Maitreya/nearmiss
- **Demo video:** https://youtu.be/RdJcO0EWhBo?si=0u6h3XCb8DwLRUP9
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2745

The problem: Pedestrian deaths from vehicle encounters are a solvable visibility problem. Cities and campuses already have drone/CCTV footage of dangerous intersections, but nobody has time to manually watch hours of footage looking for near-misses—the close calls that precede fatal collisions and reveal exactly where infrastructure (crossings, signage, sightlines) is failing people. Near-misses are the leading indicator; today they go completely unmeasured.

What NearMiss does: It's an automated near-miss detection and triage system for pedestrian-vehicle interactions, built and tested on real aerial drone footage from the FLUID dataset.

1. Detect—SAM 3.1 segments every pedestrian and vehicle per frame; a custom PET (Post-Encroachment Time) engine computes real geometric closeness and timing for every pair, flagging events as critical/serious/moderate based on how little time separated them.
2. Verify—Muse Spark visually re-inspects every flagged event's evidence frames, confirming or disputing the computer-vision read and writing a plain-English note—cutting false positives before a human ever looks.
3. Triage—A human review queue surfaces only the events that actually need judgment, with evidence frames, Spark's verdict, and now voice-note capture (via 'muse-voice-transcribe-1.0') so analysts can log decisions verbally instead of typing.
4. Report—A live "Mission Deck" dashboard (Cockpit, Analytics, Radar, Ledger tabs) gives a real-time operational view: KPIs, a spatial radar plot of where near-misses cluster, per-event Q&A ("Why was this flagged?" and "What can we do?"), and a grounded, fabrication-checked pattern-insight summarizing recurring risk patterns across all critical events.

The output isn't just a detector—it's a decision-support tool: it tells a city or campus safety team where the dangerous spots are, why they're dangerous, and what action each specific event suggests, using only real, verifiable numbers.

## More from Meta Global AI Developer Hackathon

- [Future Builders](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2742)
- [Show Me Once: Verified Receiving Agent](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2743)
- [Senators](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2744)
- [MultiplAIers](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2746)
- [Oral-lens](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2747)
- [Signoff](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/2748)

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