HyperVision

Inspiration

Computer vision is insanely powerful, but let’s be real, it’s usually locked behind research papers, PhDs, or overkill internal tools. We wanted to break that wall. Hypervision was born from the idea that advanced CV should be usable by anyone building real-world products including doctors, developers, security teams, creators, not just lab researchers.

What it does

Hypervision is a modular computer vision platform that delivers real-time tracking, detection, annotation, and AI-powered object understanding across multiple domains.

It powers applications in:

• 🏥 Medicine (surgical tool tracking)
• 🔒 Security (live monitoring & alerts)
• 🎮 Entertainment (AR chess with hand tracking)
• 🏃 Health & sports coaching
• 🗺️ Accessibility & navigation

All built on a hybrid tracking engine combining optical flow, YOLO detection, and AI validation.

How we built it

We designed Hypervision around a hybrid CV architecture:

Optical flow for fast, low-latency tracking

• YOLO for object-level detection and recovery
• AI (GPT-4.1-mini) for semantic object understanding
• Anchor-based rigid body tracking
• Kalman filtering for smooth motion
• WebSocket pipelines for real-time performance

Tech stack highlights:

• Frontend: Next.js 14, React, TypeScript
• Backend: FastAPI, Firebase Functions
• CV: OpenCV, MediaPipe, Ultralytics YOLO
• AI: OpenAI GPT-4.1-mini / GPT-4o-mini
• Real-time: WebSockets, Canvas API

Everything is modular so new applications plug straight into the core engine.

Challenges we ran into

• Keeping tracking stable when objects leave the frame
• Syncing optical flow with YOLO detections in real time
• Avoiding false re-identification across similar objects
• Balancing performance vs accuracy at 30 FPS
• Making advanced CV feel simple in the UI

Accomplishments that we're proud of

• Helping doctors save lives.
• A production-ready hybrid tracking engine
• Real-time AI-assisted object validation
• Multi-domain applications built on the same core
• Clean developer SDK (HoloRay)
• Smooth, interactive UI with live annotation
• A system that actually works outside demo conditions

What we learned

• Hybrid systems > single-model solutions
• Optical flow is cracked when validated properly
• AI is best used as a validator, not a crutch
• Real-time UX matters as much as raw CV accuracy
• Modular architecture saves your sanity long-term

What's next for HyperVision

• Model fine-tuning for medical & security domains
• Mobile support
• Multi-camera synchronization
• Edge deployment
• Plugin marketplace for custom CV modules

Built With

• fastapi
• firebase
• mediapipe
• next.js
• openai
• opencv
• react
• typescript
• websocket
• yolo