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
The Client
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.
The Challenge
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.
What We Built
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.
Technology
Powered By
OpenPose
Pose Estimation
Activity Recognition
Frame Interpolation
Cross-Platform Deployment
Resource Optimization
The Results
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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