Week 1 Alan Wright - UCF.

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Presentation transcript:

Week 1 Alan Wright - UCF

Optical Flow - Lucas Kanade Input images:

Optical Flow - Lucas Kanade Output:

Optical Flow - Lucas Kanade Output of Flow to Color:

Optical Flow - Lucas Kanade Output of both:

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!

Optical Flow - Lucas Kanade Video Script

Optical Flow - Lucas Kanade Video Script Output

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 lvl1 100x100 lvl2 50 x 50 lvl3 25 x 25 lvl4 12.5 x 12.5 lvl5 Lose a pixel!

Level 3 Level 2 Level 1

Optical Flow: Pyramids vs Non

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!

SIFT Descriptor Input: 18x18 patch

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,...,320-359) Discrepancies: Different sigma values for gaussian blur, center pixel for VLFEAT.

Sift Descriptor

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