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Robust real time object detection
1. The document presents the Viola-Jones object detection framework which uses integral images, AdaBoost learning, and a cascade classifier structure for real-time object detection, such as face detection. 2. It introduces integral images as an image representation for fast feature extraction, AdaBoost for selecting important features, and a cascade structure for increased detection speed while maintaining accuracy. 3. Experiments showed the cascade classifier approach was nearly 10 times faster than a single classifier while maintaining high detection rates, enabling real-time face detection.
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Robust real time object detection
- 1. Robust Real-Time ObjectDetectionAuthor: Paul Viola, Michael J. Jones International Journal of Computer Vision 57(2), 137–154, 2004 Presented by ERLI – M10115801
- 2. Background & Motivation •Detecting object on real time • Detecting face
- 3. Outline Introduction Image Representation –Integral Image Learning Algorithm – AdaBoost Cascade Classifier Experiment and Result
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- 5. Object Detection >> FaceDetection Paul Viola Michael J. Jones Introduction
- 6. Detection System 24x24 Detection Window The sizeof detection window will be enlarged with a certain scale Introduction
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- 8. Integral Image • Imagerepresentation • Feature • The integral image at location x,y contains the sum of the pixels above and to the left of x,y Integral Image | AdaBoost | Cascade Sum of pixel value in this area (x,y)
- 9. Integral Image • Imagerepresentation • Feature • The integral image at location x,y contains the sum of the pixels above and to the left of x,y Integral Image | AdaBoost | Cascade Sum of pixel value in this area (x,y) How to calculate D ? A B C D 1 2 3 4
- 10. Integral Image • Imagerepresentation • Feature Integral Image | AdaBoost | Cascade How to calculate D ? D = (1+4) – (2+3) A B C D 1 2 3 4 1 2 3 4
- 11. Integral Image Feature IntegralImage | AdaBoost | Cascade Detection window A B C D Detection window Detection window Detection window The value of integral image feature is the difference between the sum of the pixels within the two rectangular regions. S1 S2 |S1-S2|
- 12. Integral Image Feature IntegralImage | AdaBoost | Cascade The size and position of feature can be different. • The eye region is darker than the upper-cheeks. • The nose bridge region is brighter than the eyes.
- 13. Integral Image • Differentsize and position of integral image give so many possible features • How do we obtain the best representing features possible? • How can we refrain from wasting time on image background? (i.e. non-object) Integral Image | AdaBoost | Cascade Good Feature Not Good Feature AdaBoost…
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- 15. Weak Classifier • Consistof just 1 feature • 1 feature that can separate the image data with error rate less than 0.5 Feature 1 Feature 2 Feature 3 θ 1 θ 2 θ 3 Weak Classifier 1 Weak Classifier 2 Weak Classifier 3 1/10 = 0.1 6/10 = 0.6 3/10 = 0.3 Integral Image | AdaBoost | Cascade
- 16. Strong Classifier • Setof features/weak classifier Integral Image | AdaBoost | Cascade 1 1 1 strong 1 1 ( ) ( ) ( ) 2 0 otherwise n n nh h h x x x Weak Classifier How to build the strong classifier? AdaBoost
- 17. AdaBoost Pseudo Code • T= number of feature we choose • h(x, f,p,θ)) = A weak classifier • f = feature • θ = threshold • p = polarity indicating the direction of the inequality (less than or greater than θ) Integral Image | AdaBoost | Cascade
- 18. AdaBoost startswith a uniform distribution of “weights” over training examples. Select the classifier with the lowest weighted error (i.e. a “weak” classifier) Increase the weights on the training examples that were misclassified. (Repeat) At the end, carefully make a linear combination of the weak classifiers obtained at all iterations. AdaBoost Ilustration 1 1 1 strong 1 1 ( ) ( ) ( ) 2 0 otherwise n n nh h h x x x Slide taken from a presentation by Qing Chen, Discover Lab, University of Ottawa Integral Image | AdaBoost | Cascade
- 19. Classifier • By increasingthe number of features per classifier, we: ◦ Increase detection accuracy. ◦ Decrease detection speed. • Experiments showed that a 200 feature classifier makes a good face detector: ◦ Takes 0.7 seconds to scan an 384 by 288 pixel image. • Problem: Not real time! (At most 0.067 seconds needed). Integral Image | AdaBoost | Cascade Cascade of Classifier…
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- 21. Cascade of Classifier •Classifier is structured as several layer/hierarchy • Each layer consist of 1 strong classifier (set of weak classifiers/features) • Evaluating all input window • Ignore non-face window • Continue for processing “suspected” face window Integral Image | AdaBoost | Cascade
- 22. Cascade of Classifier •The number of feature/weak classifier in every layer is based on the value of false positive rate and detection rate determined by user • Previous layers have simpler classifier than next layer Integral Image | AdaBoost | Cascade Stage 1 Stage 2 Stage 3 … Further Processing Input Image Window Ignore/Reject Input Ignore/Reject Input Ignore/Reject Input Ignore/Reject Input Y N Y N Y N Y N
- 23. Cascade of Classifier IntegralImage | AdaBoost | Cascade Stage 1 (2 feature) N Y Stage 2 (2++ feature)
- 24. Cascade of Classifier Pseudo Code IntegralImage | AdaBoost | Cascade
- 25. Experiment • Training set •Face: 4916 labeled frontal faces •Testing / Real-time test • Face: MIT + CMU frontal face test set • Contain: 130 images with 507 labeled frontal faces
- 26. Result Cascade Classifier v.s Non CascadeClassifier Cascade classifier nearly 10x faster.
- 27. Result They use datasetMIT+CMU with 5 images removed Voting (Using 3 Viola-Jones Classifier with different parameter) ++- : Face --+ : Non Face
- 28. Failure Mode • Informalobservation suggests that the face detector can detect faces that are tilted up to about ±15 degrees in plane and about ± 45 degrees out of plane • Harsh backlighting in which the faces are very dark while the background is relatively light sometimes causes failures • Proposed algorithm will also fail on significantly occluded faces