presentation: simulating high quality video from still images
TRANSCRIPT
HIGH QUALITY SIMULATED VIDEO FROM STILL IMAGES
Advisor – Dr. Shay Har-Noy | Technical Lead – David Schmidt
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presented by Alexander Chan & Nima Hashemi
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|Typical UAV System
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1. UAV captures video of ground in real time
2. Data transmitted via airborne modem3. Data passes through 10 mb/s data link4. Video received through ground modem
and viewed
(2)
(3)
(4)
(1)
|Motivation: Pen or Marker?
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Problem:Detecting small features
in video is difficult.
Resolution: Use high resolution still
images to create simulated video.
| Comparing Video and Still Images
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Spatial versus Temporal Resolution
HD Video 1920x1080 = 2 Mp per frame. 3.2
pixel/inch Still Images
4743 x 3162 = 15 Mp per frame. 7.9 pixel/inch
| Approach
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(1)
(2) (4)
(3)
|System Overview
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|Image Stitching Algorithm
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|Image Stitching Visualized
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Extracted Feature Points (SURF) Paired Feature Points Remain (FLANN Matching & RANSAC)
Stitched Mosaic (Feature Mapping and Blurring)
A BA & B
|Demonstration
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Zooming into video for visual feature detection
Synchronization between video and image windows
|Current Limitations and Future Steps
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Increase robustness of stitching algorithm
Increase accuracy of time relation between video feed and image viewer
Optimize CPU consumption throughout system
|What We Learned
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Image Processing and User Interface
Project Management and Spiral Development
“Integration takes longer than development”
Integrating Open Source Libraries (OpenCV, IJG Library, and existing EnerView
system)
|Conclusion & Applications
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|Acknowledgements
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Dr. Shay Har-Noy, Dave Schmidt, Steve Gardner
Fran Abrams and the ViaSat HR team
OpenCV Community, IJG Community
Kevin, Andy, Brian, Ricky, and our intern family
The Antarctic Penguins