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TAIPEI | SEP. 21-22, 2016
Luke Liu, Sep 21 2016
EMBEDDED AND RELIABLE
COMPUTER VISION
2
AGENDA
About LIPS
Turnkey Solution for Reliable Computer Vision
Implementation CV on Embedded System
Benefit from Depth Data for Machine Learning
Gesture and Posture Recognition base on CNN
Applications
Conclusion
3
ABOUT LIPS
Focus on Human-Computer-Interface
Create a Real 3D World
Focus on Design Depth Camera
Focus on Parallel Computing
Focus on Machine Learning
Teach Computer to “Sense the World”
Sense the World
4
TURNKEY SOLUTION FOR RELIABLE
COMPUTER VISION
LIPS (Solution Provider) = Hardware(Depth Camera) + Middleware (Software)
Application
OpenNI Framework
Depth optimization works
Driver for Android, Linux, Mac, and Windows OS
Application Development
5
IMPLEMENTATION CV ON EMBEDDED SYSTEM
CUDA for Signal and Image Processing
Time-of-Flight Signal Processing
Image Processing for Reducing Noise
Real-Time Depth、IR and Image Data
Same Task on CPU and TK1 cost: 0.5 fps vs 60 fps
6NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
BENEFIT FROM DEPTH DATA FOR MACHINE
LEARNING
Easy to segmentation from depth data
Clue: Depth data on a object is continuous
Normalization from depth data
Clue: Reduce same object for rotate
Goal: Object recognition with “Description”
Reduce Numerous Training Data
7NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
GESTURE AND POSTURE RECOGNITION BASE
ON CNN
Video and Sponsorship for NVIDIA A.I. Robotics Competition 2016
8NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
APPLICATIONS
3D Surveillance
Augmented Reality
Virtual Reality
Automotive
Drone
PDA with depth camera in logistic
9NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
APPLICATIONS
TV/TV box NUI
Portable 3D scanner
Gaming
Digital Signage
Biometric recognition for access control system (Iris/face/gesture)
Robot vision
IoT
10NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
CONCLUSION
GPU is suitable platform for Parallel and Image Processing
Depth data will reduce searching range and provide reliable computer vision
TAIPEI | SEP. 21-22, 2016
THANK YOU

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Embedded and Reliable Computer Vision

  • 1. TAIPEI | SEP. 21-22, 2016 Luke Liu, Sep 21 2016 EMBEDDED AND RELIABLE COMPUTER VISION
  • 2. 2 AGENDA About LIPS Turnkey Solution for Reliable Computer Vision Implementation CV on Embedded System Benefit from Depth Data for Machine Learning Gesture and Posture Recognition base on CNN Applications Conclusion
  • 3. 3 ABOUT LIPS Focus on Human-Computer-Interface Create a Real 3D World Focus on Design Depth Camera Focus on Parallel Computing Focus on Machine Learning Teach Computer to “Sense the World” Sense the World
  • 4. 4 TURNKEY SOLUTION FOR RELIABLE COMPUTER VISION LIPS (Solution Provider) = Hardware(Depth Camera) + Middleware (Software) Application OpenNI Framework Depth optimization works Driver for Android, Linux, Mac, and Windows OS Application Development
  • 5. 5 IMPLEMENTATION CV ON EMBEDDED SYSTEM CUDA for Signal and Image Processing Time-of-Flight Signal Processing Image Processing for Reducing Noise Real-Time Depth、IR and Image Data Same Task on CPU and TK1 cost: 0.5 fps vs 60 fps
  • 6. 6NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE. BENEFIT FROM DEPTH DATA FOR MACHINE LEARNING Easy to segmentation from depth data Clue: Depth data on a object is continuous Normalization from depth data Clue: Reduce same object for rotate Goal: Object recognition with “Description” Reduce Numerous Training Data
  • 7. 7NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE. GESTURE AND POSTURE RECOGNITION BASE ON CNN Video and Sponsorship for NVIDIA A.I. Robotics Competition 2016
  • 8. 8NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE. APPLICATIONS 3D Surveillance Augmented Reality Virtual Reality Automotive Drone PDA with depth camera in logistic
  • 9. 9NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE. APPLICATIONS TV/TV box NUI Portable 3D scanner Gaming Digital Signage Biometric recognition for access control system (Iris/face/gesture) Robot vision IoT
  • 10. 10NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE. CONCLUSION GPU is suitable platform for Parallel and Image Processing Depth data will reduce searching range and provide reliable computer vision
  • 11. TAIPEI | SEP. 21-22, 2016 THANK YOU