1. Bohr Robot Group OpenCV ECE479 John Chhokar J.C. Arada Richard Dixon

2. Goals Learn OPENCV Implement Face Tracking Robot using Haar Cascade Implement Color Detecting Robot Implement Obstacle Avoidance Robot/Navigation

3. Face Detection OpenCV makes face detection easy using the Haar Cascade Classifier stored in a XML file. Classifier uses data stored in the XML file to classify each image The Haar sequence was proposed in 1909 by Alfred Haar Viola-Jones method is the fundamental technique used in face detection where their work models wavelets (Publish 2001, Conference on Computer Vision and Pattern Recognition) Haar Feature Rectangles are used to detect Haar features. Computations of Haar Rectangles are performed by a technique called an Integral Image. The pre-processing of the image to significantly increase Haar like features.

4. Face Detection 4 = A + B + C + D 2 = A + B 3 = A + C 1 = A Integrals Image Value, the sum of all the pixels above it and to the left D = (x 4,y 4 ) – (x 2,y 2 ) – (x 3,y 3 ) + (x 1,y 1 ) Sum of all pixels inside of a rectangle needs only 4 array references. S = A – B – C + D. Two rectangle sums = 8.

5. Face Detection OpenCV Haar Features Sum of the pixels which lie within the white rectangles are subtracted from the sum of the pixels within the grey rectangles Rectangle features can be compiled very quickly.

6. Face Detection Filtering keys on differences in facial features. Eye region is often darker than cheeks. Intensity across bridge of nose and eye region. All filters must pass for image to be recognized as face. If one fails image is not considered

7. Face Detection Demo

8. Color Detection cvCvtColor cvInRangeS cvSmooth CvSeq* circles = cvHoughCircles

9. Color Detection

10. Color Detection #2

11. Obstacle Avoidance/Self Navigation Work in progress The goal of this portion of our project is to have the Bohr Robot self navigate a hallway or corridor and possibly negotiate a right or left turn. To accomplish this that we implement a series of functions and transformations – Canny edge detection – Hough Line transform

12. How Color Detection Works Colors are represented by a combination of Red Green and Blue We simplify the color scale by defining colors in a 24bit scale – Red, Blue, and Green are represented each by 8 bits Bitmap images can best be described as a matrix populated by the 24bit values. – We use a reference value for a color and start matching any color that color that corresponds to that value

13. Simplified Algorithm Below is the simplified algorithm on how OpenCV detects colors Get image size – Get X and Y values of images While I <= X – While J <= Y If Reference == Color – Then match == 1; – Else match == 0; J = J+1; End while – End while

14. How the Program Works We use the X value obtained from the match – Average out the values of X in the frame Move the motor towards X – Done by defining the center as the reference – We then move the motor left or right dependent on the reference point

15. Problems encountered Latency is too slow – Movement of motor dependant an event that already occurred. – Bad feedback Algorithm is not optimized – Using sleep instead of polling Workaround is to delay sending of commands – Average out movement.

16. Obstacle Avoidance/Self Navigation Laplace function Canny edge detection – Contours – Formed by thresholding image pixels – If gradient is of pixel lower it is rejected higher it is accepted Between accepted if connected to a pixel in has a higher gradient – 2:1 or 3:1

17. Obstacle Avoidance/Self Navigation cvCvtColor(frame, grey, CV_BGR2GRAY); cvCanny( grey, edges,50, 150, 3);

18. Obstacle Avoidance/Self Navigation Hough Transformation Standard Hough Transformation(SHT) – Uses the slop intercept form (y = mx + b) – Range + infinity Probabilistic Hough Transformation (PPHT) – P=x cos(t) + y sin(t) – Accumulates a fraction of points

19. Obstacle Avoidance/Self Navigation cvHoughLines2( edges, storage, CV_HOUGH_PROBABILISTIC, 1, CV_PI/180, 50, 50, 10 );

20. Obstacle Avoidance/Self Navigation What Next Implementation of our decision algorithm – Calculate the slope the lines in our ROI (~ 2 ft) If there are no line in ROI – Move forward If slope is -.1 to.1 – stop( object in front) – Cal slope (ROI) Left (~1ft) If no line then turn Slope? – Cal slope (ROI) Right (~1ft) …

21. Obstacle Avoidance/Self Navigation

22. References Paul Viola and Michael Jones Original Paper 2001 at Computer Vision and Pattern Recognition Conference http://research.microsoft.com/enus/um/people/viola/Pubs/D etect/violaJones_CVPR2001.pdf Cognitive Robotics Seeing with OpenCV Finding Faces http://www.cognotics.com/opencv/servo_2007_series/part_2 /index.html