Transcript Fast Human Detection Using a Novel Boosted Cascading

Fast Human Detection Using a Novel Boosted
Cascading Structure With Meta Stages
Yu-Ting Chen and Chu-Song Chen, Member, IEEE
INTRODUCTION
 TECHNIQUES for detecting humans in images have a
wide variety of applications, such as
video surveillance，
smart rooms，
content-based image/video retrieval，
and intelligent transportation systems (ITS)。
ABSTRACT
 We propose a method that can detect humans in a
single image based on a novel cascaded structure
.
 USE : intensity-based rectangle features ，
gradient-based 1-D features，
real AdaBoost algorithm，
a novel cascaded structure
(standard boosted cascade)，
meta-stages。
REAL ADABOOST AND FEATURE POOL
 Intensity-Based Features:
:denotes the rth type
rectangle feature (r=1~10)
and
are the
Illumination summations in
the white and black regions,
:
is the feature value in block
In each block,a feature value can be calculated
The integral-image method
is used for fast evaluation of
these features,but the results
for human detection are not
satisfactory.so add discrimina
REAL ADABOOST AND FEATURE POOL
 Gradient-Based Features:
HOG:
First, the representation is too complex to evaluate, resulting in a slow detection
speed.
Second,all the dimensions of a HOG feature vector are employed simultaneously,
so it is not possible to just use some of them to achieve efficient detection.
Third, its computation cost is high since it uses a Gaussian-kernel SVM instead of
linear SVM.
EOH:
A EOH feature can only characterize one orientation at a time, and it is represented
by a real value.
Many EOH features (with respect to different orientations)can be extracted from an
image region, but each feature is only 1-D.
REAL ADABOOST AND FEATURE POOL
 EOF Features:
First: The gradient image is calculated from the original image
by convolving the edge operator.
REAL ADABOOST AND FEATURE POOL
Second:
To compute the EOH features, the pixel gradient magnitude m and
gradient orientation θ of each pixel p at location (x,y) in block Bi.
Gx: gradients in the horizontal directions
Gy: gradients in the vertical directions
The gradient orientation is evenly divided into K bins over 0 to 180 .
The sign of the orientation is ignored; thus,the orientations between
180 to 360 are deemed the same as those between 0 and 180 .
REAL ADABOOST AND FEATURE POOL
Third:
The gradient orientation histograms Ｅi,k in each orientation bin K of
block Bi are obtained by summing all the gradient magnitudes whose
orientations belong to bin K in Bi.
Fourth:
is the feature value of the Kth ( K = 1~ K) EOH feature in block Bi.
ε is a small positive value that avoids the denominator being zero.
REAL ADABOOST AND FEATURE POOL
Gradient-Based ED (edge-density) feature :
For a block , an ED (edge-density) feature is defined as the average
gradient magnitude
is the ED feature value in Bi and ai is the area of Bi
Similar to the rectangle features, the integral-image method can be
employed for fast evaluation of the ED features.
Combined Feature Pool:
r = 1~10, k = 1~K
REAL ADABOOST AND FEATURE POOL
 real AdaBoost algorithm :
Given input data z and its feature
Value f(z), the weak learner
output h(z)
After selecting T weak classifiers,the strong classifier of Real AdaBoost can
be expressed as
α is a threshold
A high confidence value implies
that the input data is likely
to be a positive sample.
CASCADING FEED-FORWARD CLASSIFIERS
A.Contains S stages and Ai is referred to as an AdaBoost classifier in the ith stage.
B.In this cascaded structure, detection windows that do not contain humans
C.To find an object of unknown position and size in an image usually involves
a brute-force search of all possible sites and scales in the image. Since there
are usually far more negative windows than positive windows to detect in an
image, saving on the detection time of the negative windows increases the
overall efficiency of the object detector.
DSince more difficult negative examples are used for training in later stages.
In the current stage will not be used in later stages.
CASCADING FEED-FORWARD CLASSIFIERS
To train a cascaded structure, the goals of the minimum detection
rate of positive examples, di, and the maximum false-acceptance
rate of negative examples,fi , are set for each stage Ai.
CASCADING FEED-FORWARD CLASSIFIERS
 Adding Meta-Stages :
“A ” and “M ” denote the AdaBoost stages and meta-stages
Meta-stages: 2-D space
Mi: 2-D vector
CASCADING FEED-FORWARD CLASSIFIERS
 Meta-Stage Classifier:
 we choose the linearSVM(LSVM) as the meta-stage
classifier because of its high generalization
ability and efficiency in evaluation.
 ω is the 2-D normal vector of the plane and
 β is the offset from the origin.
 The confidence value of the meta-stage for data
is defined as
RESULTS




rectangle features = Rec-Cascade (625)
EOH features = EOH-Cascade (584)
ED features = ED-Cascade (2492)
a combination of rectangle and EOH features = RecEOHCascade(325)
 a combination of them as feature = RecEOHED-Cascade (310)
RESULTS
In our experiments, the maximum FPPW HOG add META than RecEOHED-Cascade
values are about 10^(-3) for most of the
MetaCascade-2D greatest
cascaded approaches compared. Since
there are far more negative windows than
the positive windows in an image, a
detector shall have a very low false
positive rate (e.g., under 10^(-3)), or it
miss rate versus
might not be practically useful.
false positives per window (FPPW)
RESULTS
In our experiments, we consider thefollowing three forms: ,
2-D meta classifiers ( n1 = 2, ni = 1 )
3-D meta classifiers ( n1 = 3, ni = 2 )
4-D meta classifiers ( n1 = 4, ni = 3 )
for a 320X240 testing image, the average processing speeds
MetaCascade-2D = 8.61 fps (243)
MetaCascade-3D = 8.52 fps (230)
MetaCascade-4D = 8.44 fps (201)
HOG-MetaCascade-2D = 6.12 fps (516)
RESULTS
MetaCascade-2D = 8.61 fps
MetaCascade-3D = 8.52 fps
MetaCascade-4D = 8.44 fps
HOG-MetaCascade-2D = 6.12 fps
HOG-LSVM (0.91 fps)
RESULTS
Thank you for your
listening !
2008.10.21