PowerPoint 프레젠테이션
Changjae Oh
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Computer Vision
– Detection1: Pedestrian detection –
Semester 1, 22/23
• Overview
• Dalal-Triggs (pedestrian detection)
̶ Histogram of Oriented Gradients
̶ Learning with SVM
Object Detection
• Focus on object search: “Where is it?”
• Build templates that differentiate object patch from background patch
Non-Object?
Challenges in modeling the object class
Illumination Object pose ‘Clutter’
Intra-class
appearance
Occlusions Viewpoint
[K. Grauman, B. Leibe]
Challenges in modeling the non-object class
Localization
Confused with
Similar Object
Confused with
Dissimilar ObjectsMisc. Background
Detections
Object Detection Design challenges
• How to efficiently search for likely objects
̶ Even simple models require searching hundreds of thousands of positions and scales.
• Feature design and scoring
̶ How should appearance be modeled?
̶ What features correspond to the object?
• How to deal with different viewpoints?
̶ Often train different models for a few different viewpoints
General Process of Object Detection
Specify Object Model
Generate Hypotheses
Score Hypotheses
Resolve Detections
What are the object
parameters?
Specifying an object model
1. Statistical Template in Bounding Box
̶ Object is some (x,y,w,h) in image
̶ Features defined with respect to bounding box coordinates
Image Template Visualization
Images from Felzenszwalb
Specifying an object model
2. Articulated parts model
̶ Object is configuration of parts
̶ Each part is detectable
Images from Felzenszwalb
Specifying an object model
3. Hybrid template/parts model
Detections
Template Visualization
Specifying an object model
4. 3D-ish model
̶ Object is collection of 3D planar patches under affine transformation
Specifying an object model
4. Deformable 3D model
̶ Object is a parameterized space of shape/pose/deformation of class of 3D object
Why not just pick the most complex model?
• Inference is harder
̶ More parameters
̶ Harder to ‘fit’ (infer / optimize fit)
̶ Longer computation
, Leveraging MoCap Data for Human Mesh Recovery, Arxiv 2021
General Process of Object Detection
Specify Object Model
Generate Hypotheses
Score Hypotheses
Resolve Detections
Propose an alignment of the
model to the image
Generating hypotheses
1. 2D template model / sliding window
̶ Test patch at each location and scale
Generating hypotheses
1. 2D template model / sliding window
̶ Test patch at each location and scale
Note – Template did not change size
Each window is separately classified
Generating hypotheses
2. Region-based proposal
̶ Arbitrary bounding box + image ‘cut’ segmentation
General Process of Object Detection
Specify Object Model
Generate Hypotheses
Score Hypotheses
Resolve Detections
Mainly gradient-based features, usually based
on summary representation, many classifiers.
General Process of Object Detection
Specify Object Model
Generate Hypotheses
Score Hypotheses
Resolve each proposed object
based on the whole set
Resolving detection scores
1. Non-max suppression
Score = 0.1
Score = 0.8 Score = 0.8
Resolving detection scores
2. Context/reasoning
̶ Via geometry
̶ Via known information or prior distributions
̶ Non-max suppression
Hoiem et al. 2006
: Person detection with HOG & linear SVM
Histograms of Oriented Gradients for Human Detection,,, International Conference on Computer Vision & Pattern Recognition – June 2005
http://lear.inrialpes.fr/people/dalal
http://lear.inrialpes.fr/people/triggs
Statistical Template
• Object model = sum of scores of features at fixed positions!
+3 +2 -2 -1 -2.5 = -0.5
+4 +1 +0.5 +3 +0.5= 10.5
Non-object
Example: Dalal-Triggs pedestrian detector
1. Extract fixed-sized (64×128 pixel) window at each position and scale
2. Compute HOG (histogram of gradient) features within each window
3. Score the window with a linear SVM classifier
4. Perform non-maxima suppression to remove overlapping detections w
ith lower scores
and, Histograms of Oriented Gradients for Human Detection, CVPR05
27Slides by
and, Histograms of Oriented Gradients for Human Detection, CVPR05
• Tested with
̶ Grayscale
• Gamma Normalization and Compression
̶ Square root
Slightly better performance vs. grayscale
Very slightly better performance vs. no adjustment
uncentered
cubic-corrected
Histogram of Oriented Gradients
̶ Votes weighted by magnitude
̶ Bilinear interpolation between cells
Orientation: 9 bins (for un
signed angles 0 -180)
Histograms over
k x k pixel cells
(4 bins shown)
Normalize with respect to surrounding cells
e is a small constant
(to remove div. by zero on empty bins)
Rectangular HOG (R-HOG)
How to normalize?
– Concatenate all cell responses from neighboring
blocks into vector.
– Normalize vector.
– Extract responses from cell of interest.
– Do this 4x for each neighbor set in 2×2.
# features = 15 x 7 x 9 x 4 = 3780
# orientations
# normalizations by
neighboring cells
pos w neg w
Training data classes
pedestrian
Strengths/Weaknesses of Statistical Template Approach
• Strengths
̶ Works very well for non-deformable objects with canonical orientations: faces, cars, p
edestrians
̶ Fast detection
• Weaknesses
̶ Not so well for highly deformable objects or “stuff”
̶ Not robust to occlusion
̶ Requires lots of training data
Changjae Oh
Computer Vision
– Detection2: Face detection –
Semester 1, 22/23
• Overview
• Viola-Jones (face detection)
̶ Boosting for learning
̶ Decision trees
Consumer application: Apple iPhoto
• Things iPhoto thinks are faces
Challenges of face detection
• Sliding window = tens of thousands of location/scale evaluations
̶ One megapixel image has ~106 pixels, and a comparable number of candidate face lo
• Faces are rare:0–10 per image
̶ For computational efficiency, spend as little time as possible on the non-face windows
• For 1 Mpix, to avoid having a false positive in every image,
our false positive rate has to be less than 10−6
The Viola/Jones Face Detector
• A seminal approach to real-time object detection.
• Training is slow, but detection is very fast
• Key ideas:
1. Integral images for fast feature evaluation
2. Boosting for feature selection
3. Attentional cascade for fast non-face window rejection
P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. CVPR 2001.
P. Viola and M. Jones. Robust real-time face detection. IJCV 57(2), 2004.
http://research.microsoft.com/en-us/um/people/viola/pubs/detect/violajones_cvpr2001.pdf
http://www.vision.caltech.edu/html-files/EE148-2005-Spring/pprs/viola04ijcv.pdf
1. Integral images for fast feature evaluation
• The integral image computes a
value at each pixel (x,y) that is
the sum of all pixel values above
and to the left of (x,y), inclusive.
• This can quickly be computed in
one pass through the image.
• ‘Summed area table’
Computing the integral image
Current pixel
Region already
Computing the integral image
• Cumulative row sum: s(x, y) = s(x–1, y) + i(x, y)
• Integral image: ii(x, y) = ii(x, y−1) + s(x, y)
ii(x, y-1)
Python: ii = np.cumsum(i)
Computing sum within a rectangle
• Let A,B,C,D be the values of the integra
l image at the corners of a rectangle
• The sum of original image values within
the rectangle can be computed as:
̶ sum = A – B – C + D
Only 3 additions are required
for any size of rectangle!
Integral Images
• ii = cumsum(cumsum(im, 1), 2)
ii(x,y) = Sum of the values in the grey region
SUM within Rectangle D is
ii(4) – ii(2) – ii(3) + ii(1)
Integral Images- example
• Find the integral image of the figure below and computer the sum of pix
els in the grey region based on the integral image.
ii(4) – ii(2) – ii(3) + ii(1)
= 42 – 10 – 4 + 1
Features that are fast to compute
• “Haar-like features”
̶ Differences of sums of intensity
̶ Computed at different positions and scales
within sliding window
Haar wavelet
Two-rectangle features Three-rectangle features Etc.
CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=801361
Image Features
“Rectangle filters”
Value =∑(pixels in white area)
– ∑(pixels in black area)
Computing a rectangle feature
But these features are rubbish…!
Yes, individually they are ‘weak classifiers’
•Jargon: ‘feature’ and ‘classifier’ are used interchangeably here.
Also with ‘learner’.
But, what if we combine thousands of them…
Two-rectangle features Three-rectangle features Etc.
How many features are there?
• For a 24×24 detection region, the number of possible rectangle features
is ~160,000!
How many features are there?
• For a 24×24 detection region, the number of possible rectangle features
is ~160,000!
• At test time, it is impractical to evaluate the entire feature set.
• Can we learn a ‘strong classifier’ using just a small subset of all possible f
2. Boosting for feature selection
Slide credit:
Initially, weight each training example equally.
Weight = size of point
In each boosting round:
Find the weak classifier,
trained for each feature,
that achieves the lowest
weighted training error.
Raise the weights of
training examples
misclassified by
current weak classifier.
2. Boosting for feature selection
Classifier 1
Slide credit:
In each boosting round:
Find the weak classifier,
trained for each feature,
that achieves the lowest
weighted training error.
Raise the weights of
training examples
misclassified by
current weak classifier.
Boostingillustration
In each boosting round:
Find the weak classifier,
trained for each feature,
that achieves the lowest
weighted training error.
Raise the weights of
training examples
misclassified by
current weak classifier.
Boostingillustration
Classifier 2
Boostingillustration
In each boosting round:
Find the weak classifier,
trained for each feature,
that achieves the lowest
weighted training error.
Raise the weights of
training examples
misclassified by
current weak classifier.
In each boosting round:
Find the weak classifier,
trained for each feature,
that achieves the lowest
weighted training error.
Raise the weights of
training examples
misclassified by
current weak classifier.
Boostingillustration
Classifier 3
Compute final classifier as
linear combination of all weak
classifier.
Weight of each classifier is
directly proportional to its
Boosting illustration
Exact formulas for re-weighting and combining weak learners
depend on the particular boosting scheme (e.g., AdaBoost).
Y. Freund and R. Schapire, A short introduction to boosting,
Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September, 1999.
http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/FreundSc99.ps
Boosting for face detection
• First two features selected by boosting:
̶ The first feature measures the difference in intensity between the region of the eyes a
nd a region across the upper cheeks. The feature capitalizes on the observation that t
he eye region is often darker than the cheeks.
̶ The second feature compares the intensities in the eye regions to the intensity across
the bridge of the nose.
The two features are shown in the t
op row and then overlayed on a typi
cal training face in the bottom row.
Feature selection with boosting
• Create a large pool of features (180K)
• Select discriminative features that work well together
• “Weak learner” = feature + threshold + ‘polarity’
• Choose weak learner that minimizes error on the weighted training set,
then reweight
window Learner weight
Weak learner
Final strong learner
value of rectangle feature
‘polarity’ = black or white region flip
Boosting: Pros and Cons
• Advantages of boosting
̶ Integrates classifier training with feature selection
̶ Complexity of training is linear instead of quadratic in the number of training examples
̶ Flexibility in the choice of weak learners, boosting scheme
̶ Testing is fast
• Disadvantages
̶ Needs many training examples
̶ Training is slow
Fast classifiers early in cascade which reject many negative
examples but detect almost all positive examples.
Slow classifiers later, but most examples don’t get there.
Cascade for Fast Detection
h1(x) > t1?
h2(x) > t2?
hn(x) > tn?
Attentional cascade
• Chain classifiers that are progressively more complex and have lower fal
se positive rates:
Receiver operating characteristic
SUB-WINDOW
Classifier 1
Classifier 3
Classifier 2
Attentional cascade
• The detection rate and the false positive rate of the cascade are found by m
ultiplying the respective rates of the individual stages
• A detection rate of 0.9 and a false positive rate on the order of 10−6 can be
achieved by a 10-stage cascade if each stage has a detection rate of 0.99 (0
.9910 ≈ 0.9) and a false positive rate of about 0.30 (0.310 ≈ 6×10−6)
SUB-WINDOW
Classifier 1
Classifier 3
Classifier 2
Training the cascade
• Set target detection and false positive rates for each stage
• Keep adding features to the current stage until its target rates have been
̶ Need to lower boosting threshold to maximize detection
(as opposed to minimizing total classification error)
̶ Test on a validation set
• If the overall false positive rate is not low enough, then add another stage
• Use false positives from current stage as the negative training examples f
or the next stage
The implemented system
• Training Data
̶ 5000 faces
• All frontal, rescaled to
24×24 pixels
̶ 300 million non-faces
• 9500 non-face images
̶ Faces are normalized
• Scale, translation
• Many variations
̶ Across individuals
̶ Illumination
Viola-Jones details
• 38 stages with 1, 10, 25, 50 … features
̶ 6061 total used out of 180K candidates
̶ 10 features evaluated on average
• Training Examples
̶ 4916 positive examples
̶ 10000 negative examples collected after each stage
• Scanning
̶ Scale detector rather than image
̶ Scale steps = 1.25(factor between two consecutive scales)
̶ Translation 1*scale (# pixels between two consecutive windows)
• Non-max suppression: average coordinates of overlapping boxes
• Train 3 classifiers and take vote
Viola-Jones Results
MIT + CMU face dataset
Speed = 15 FPS (in 2001)
Boosting for face detection
• A 200-feature classifier can yield 95% detection rat
e and a false positive rate of 1 in 14084
Not good enough!
Receiver operating characteristic (ROC) curve
Output of Face Detector on Test Images
Summary: Viola/Jones detector
• Rectangle features
• Integral images for fast computation
• Boosting for feature selection
• Attentional cascade for fast rejection of negative windows
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