Presentation: Simulating High Quality Video from Still Images
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Developed method of creating a high spatial resolution video from a series of panorama-based still images. Independently implemented dynamic image stitching in C (OpenCV) and integrated into existing software.
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Presentation: Simulating High Quality Video from Still Images
- 1. 1 ViaSat Proprietary Information HIGH QUALITY SIMULATED VIDEO FROM STILL IMAGES presented by Alexander Chan & Nima Hashemi Advisor – Dr. Shay Har-Noy | Technical Lead – David Schmidt
- 2. |Typical UAV System 1. UAV captures video of ground in real time 2. Data transmitted via airborne modem 3. Data passes through 10 mb/s data link 4. Video received through ground modem and ViaSat Proprietary Information viewed (2) (3) (4) (1)
- 3. |Motivation: Pen or Marker? ViaSat Proprietary Information 3 Problem: Detecting small features in video is difficult. Resolution: Use high resolution still images to create simulated video.
- 4. | Comparing Video and Still Images ViaSat Proprietary Information 4 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
- 5. | Approach ViaSat Proprietary Information 5 (1) (3) (2) (4)
- 6. |System Overview ViaSat Proprietary Information 6
- 7. |Image Stitching Algorithm ViaSat Proprietary Information 7
- 8. |Image Stitching Visualized ViaSat Proprietary Information 8 Extracted Stitched Paired Mosaic FFeeaattuurree (Feature PPooiinnttss R(SeUmRaFin) Mapping (FLANN and Blurring) Matching & RANSAC) A B A & B
- 9. |Demonstration ViaSat Proprietary Information 9 Zooming into video for visual feature detection Synchronization between video and image windows
- 10. |Current Limitations and Future Steps ViaSat Proprietary Information 10 Increase robustness of stitching algorithm Increase accuracy of time relation between video feed and image viewer Optimize CPU consumption throughout system
- 11. |What We Learned ViaSat Proprietary Information 11 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)
- 12. |Conclusion & Applications ViaSat Proprietary Information 12
- 13. |Acknowledgements ViaSat Proprietary Information 13 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
Editor's Notes
- Let the audience know who we are Name Major Year Aspirations (tech entrepreneur, business development ?)
- COINSTRAINTS: Video feed from UAV inherently mediocre quality: 10 mb/s data link limits video quality Zooming for video is sub-optimal Take this sample video frame: With regards to being able to detect small changes in landscape – it’s easy to see that there’s significant room for improvement.
- Mention Visionary Steve Gardner- “penguins”
- We take the conventional Video Feed and sort of turn it on its head. Mapping video feed to mosaic coordinates based on timestamp Displaying selected region in Image Viewer at high resolution and zoom Mosaic continues to be stitched dynamically as video plays Arrows represent direction of motion. Note that the small rectangle’s movement translates to the movement the image viewer
- Building a dialogue box off of the existing EnerView System
- I will go over this briefly. Start with a directory of images. Downsample images to decrease computation time. Extract features Find matching features between images RANSAC filters out bad matches Create homography out of accurate feature relationships -> this describes the overall translation of one image into another Finally apply the homography and combine the images -> through a short mapping and blurring algorithm
- Robustness – meaning it can extrapolate relationships between two images even if they are rotated, stretched, zoomed at different angles, etc. As of now, we are simply using a linear approach. Optimizing – multithreading, further downsampling of images,
- Various algorithms Zooming and aspect ratios Gained extensive experience in agile development cycle, unit testing, and core development
- A static image mosaic approach absolutely allows users to visually detect features more easily. Ecological surveying Disaster relief IED Detection