1. Week 1
Alan Wright - UCF

2. Optical Flow - Lucas Kanade
Input images:

3. Optical Flow - Lucas Kanade
Output:

4. Optical Flow - Lucas Kanade
Output of Flow to Color:

5. Optical Flow - Lucas Kanade
Output of both:

6. Optical Flow - Lucas Kanade Video Script
inputPath = '..\Sequence';
for i = 133:2:183
I = [inputPath '\' sprintf('%04d.jpeg', i)];
I2 = [inputPath '\' sprintf('%04d.jpeg', i+1)];
opticflow(I, I2);
print('-djpeg', sprintf('%04d.jpg', i) );
close all;
end
This is your function!

7. Optical Flow - Lucas Kanade Video Script

8. Optical Flow - Lucas Kanade Video Script
Output

9. Optical Flow - Lucas Kanade w/ Pyramids
Hardcoded 3 levels
Changed to variable length of level using MATLab loop.
Difficulties encountered: Resolution must be evenly divisible by 2, for each level.
Ex: 200x200 lvl x100 lvl x 50 lvl x 25 lvl x 12.5 lvl5 Lose a pixel!

10. Level 3
Level 2
Level 1

11. Optical Flow: Pyramids vs Non

12. Optical Flow: Pyramids vs Non
With Pyramids: more uniform motion, more accurate, takes more computational time.
Incorrect motion exaggerated? Currently using a 3x3 square, may need to be larger for more accurate results.
Fix: If the shift is outside the 3x3, do not calculate!

13. SIFT Descriptor
Input: 18x18 patch

14. SIFT Descriptor 2 pixel border for a 16x16 patch.
Split into 16, 4x4 squares.
Based on each pixels direction, it's magnitude is placed in a bin.
My output: 16x8 matrix. 16 squares, each with 8 bins (0-44, 45-89,..., )
Discrepancies: Different sigma values for gaussian blur, center pixel for VLFEAT.

15. Sift Descriptor

16. Possible Projects?
Large Scale Real World Face Recognition in Movie Trailers - Enrique Ortiz
Web Assisted Object Detection for Outdoor Scenes - Amir R. Zamir
Crowd counting by estimation of texture repetition - Imran Saleemi
3D Joint Localization for Gesture Recognition