Embedded and Reliable Computer Vision
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Transcript of Embedded and Reliable Computer Vision
TAIPEI | SEP. 21-22, 2016
Luke Liu, Sep 21 2016
EMBEDDED AND RELIABLE COMPUTER VISION
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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
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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
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TURNKEY SOLUTION FOR RELIABLE COMPUTER VISION
LIPS (Solution Provider) = Hardware(Depth Camera) + Middleware (Software)
Application
OpenNI Framework
Depth optimization worksDriver for Android, Linux, Mac, and Windows OS
Application Development
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IMPLEMENTATION CV ON EMBEDDED SYSTEMCUDA 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
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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
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GESTURE AND POSTURE RECOGNITION BASE ON CNN
Video and Sponsorship for NVIDIA A.I. Robotics Competition 2016
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APPLICATIONS
3D Surveillance
Augmented Reality
Virtual Reality
Automotive
Drone
PDA with depth camera in logistic
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APPLICATIONS
TV/TV box NUI
Portable 3D scanner
Gaming
Digital Signage
Biometric recognition for access control system (Iris/face/gesture)
Robot vision
IoT
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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