Pose Estimation
4 FPS pose estimation using TV hardware for trillion dollar co.
Reduce Costs
Headline Impact
4 FPS
Real-Time Pose Estimation on Resource-Constrained TV Hardware
Consumer Electronics Computer Vision Pose Estimation
4 FPS
Real-time pose estimation achieved on TV hardware with 4GB RAM
3 Platforms
Deployed across iOS, Android, and Tizen
Real-Time
Exercise rep counting and posture tracking via webcam

Confidential — Large Smartphone Manufacturer

A large smartphone manufacturer building a health application for their Smart TV platform — a virtual fitness trainer that counts exercise repetitions and tracks posture through the TV's webcam.

Pose Estimation on TV Hardware with Only 4GB RAM

Running pose estimation at the required 4 frames per second on TV hardware with only 4GB RAM — a fraction of what phones offer. Standard models were too resource-hungry, but accuracy couldn't be sacrificed because the system needed to correctly count reps and detect posture errors in real time.

Cross-Platform Pose Estimation with Custom Interpolation

1. Model Training
Trained pose estimation and activity recognition models on proprietary data using OpenPose architecture.
2. Interpolation Algorithm
Developed a custom algorithm that approximates poses from previous frames, enabling lower-resolution processing without sacrificing accuracy.
3. Cross-Platform Deployment
Deployed optimized models across iOS, Android, and Tizen with platform-specific performance tuning.
4. Virtual Trainer Integration
Integrated with the health app to deliver real-time rep counting, posture feedback, and workout guidance.

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OpenPose Pose Estimation Activity Recognition Frame Interpolation Cross-Platform Deployment Resource Optimization

4 FPS on Constrained Hardware — Across Three Platforms

Achieved the 4 FPS target on resource-constrained TV hardware while maintaining accurate rep counting and posture tracking. Deployed across three platforms — iOS, Android, and Tizen — for the manufacturer's health ecosystem.

"The major challenge was running pose estimation at 4 frames per second on TV hardware. Our interpolation algorithm made the impossible possible."

— Engineering Team

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