Development of a Whitecap Measurement System

1. Development of a Whitecap Measurement System Student: Garry Higgins Supervisor: Dr. Edward Jones Co-Supervisor: Dr. Martin Glavin

2. Project Outline • Development of a whitecap measurement system for Mace Head • Core Components: • Image Capture • Image Processing • Data Handling • Joint EE and ECI project • Why did I choose this project?

3. Why I Chose This Project • Interesting Subject • Outdoors Aspect • Projectcoveredarangeofinterestingtopics

4. Hardware • CCTV Cameras • SLR Camera

5. CCTV Cameras • 2 x Marshall 1/3” CCD Cameras • Geovision GV-600 Surveillance • 450 T.V. lines  340x240 resolution at 30fps • BNC Connectors • Connected to P4 2.4GHz PC with 504MB Ram

6. SLR Type Cameras • Canon 350D – Sigma AP0DG lens 70-300mm • Nikon D70 – DX lens 18-70mm • Picture resolution of 3456x2304

7. Nikon D70 • Image exposure varies from picture to picture

8. Canon 350D • Consistent exposure for each picture • Sigma zoom lens allowed for testing of different zoom levels • Increase in zoom causes increase in effect of tower shaking • Tested capturing area to north-east • Required zoom over 200mm focal length • No significant advantage

9. Software • Matlab code • Perl Scripts • Mplayer

10. MATLAB • Main percentage coverage algorithm • Get % white for each threshold level 0.01 -> 1.0 • Get first differential and apply MATLAB “smooth” function • Get second differential and apply “smooth” function • Right -> Left : Desired threshold when it goes above 0.01 • Compiled to stand alone executable file

11. Pre-processing • Horizon and surf-zone need to be cropped • Mplayer -vf crop=[w:h:x:y] –vojpeg:quality=100 –frames a –sstep b • Combination of Perl scripts and batch file to parse avi files • Compiled MATLAB code called • Event| Year | Month | Day | Hour | Min(5s) | Secs | Cam |.avi • Output results to txt file based on avi name

12. Algorithm Accuracy • Compare results with those obtained by expert in the field on sample data • Unsmoothed, single smoothed and double smoothed results compared • Mean and coefficient of correlation between each version and original results calculated

13. Algorithm Accuracy Original: Mean: 9.545170e-001 Unsmoothed: Mean: 1.124245e+000 Single Smooth: Mean: 1.543157e+000 Double Smooth: Mean: 1.342003e+000 Coefficients of Correlation: Original vs Unsmoothed: 6.855651e-001 Original vs Single Smooth: 9.029801e-001 Original vs Double Smooth: 9.387241e-001

14. CCTV Data • Cameras recorded for month of March from 11:00am to 12:00pm • Videos retrieved and percentage coverage for each day calculated • Wind speed for month obtained from MET Eireann

15. Camera Correlation • Mean and coefficient of correlation calculated for each camera • Cam01 mean = 1.714857e+000 • Cam04 mean = 6.353763e-001 • Coefficient of Correlation = 8.348656e-001 • Correlation of +0.83 low for data being compared

16. Reasons for differences • Cam01: rain causing droplets of water on lens • Fog • Interference

17. Whitecap vs Wind Speed • Daily average of wind speed and whitecap coverage calculated • 8th -> 14th, 16th, 17th, 19th and 20th • Wind speeds parsed from MET Eireann info • Scatter plot for each camera plotted using MATLAB “loglog” function • Power law relationship fitted in least square relationship

18. Processing Efficiency • Needs to process data in (pseudo) real-time • Perl includes a “Benchmark” module • Adapted video splitting script to include benchmarking results • Run on sample of 50 videos • ~2.7x efficient

19. Avenues for Future Development • CCTV System: • Remove interference • Cover cam01 from rain • Fog/Environmental • SLR System: • USB physical limitations • Camera control software • Housing • Algorithm Efficiency

20. Questions??