SimbaGPT v2.0 – Markerless Motion Capture Meets Smart Advertising
We’ve officially transitioned from prototype to production-ready.
V1.0 of SimbaGPT relied on a combination of Hikvision PTZ cameras and OBS Studio, enabling basic crowd interaction through motion-based overlays. It served as our initial proof-of-concept for integrating physical audience behaviour into dynamic, screen-based advertising.
Today, with V2.0, we're introducing:
- Markerless motion capture using Luxonis OAK-D PoE cameras
- NVIDIA Jetson-accelerated edge inference for ultra-low latency ad switching
- Google Maps Platform APIs for contextual, location-based engagement
- A proprietary ML pipeline for real-time human pose estimation and attention tracking
- Integration with Rydlr’s Ad Decision Engine for adaptive campaign delivery
Our solution no longer requires wearable sensors — we use computer vision alone to understand audience behaviour and trigger content transformations on public-facing screens. It's real-time, scalable, and geographically aware.
Built initially with NVIDIA DeepStream (V1.0), the system now supports Jetson NX and OAK edge hardware, making it deployable in urban mobility hubs, retail stores, and sports arenas.
Deployed across Nairobi, Cape Town, and Kigali, SimbaGPT is redefining out-of-home advertising in emerging markets through intelligent interactivity.
Created by: Ted Iro Opiyo
Built with: Google Maps Platform APIs, Luxonis OAK-D PoE, Jetson Xavier NX, NVIDIA DeepStream, Digital Ocean, Python, ONNX, React
Organization: Rydlr Cloud Services Ltd.
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