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incremental p can extend with Taylor series

a condition for corner

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choose best feature/
image patch for tracking

image alignment

Mean-shift Tracking

move window throug whole image and calculate SSD/SAD tofind the most similar patch

#2 pyramid template matching

template matching

first scale the image, and then apply template matching to each scale

#3 model refinement
precondition have a good intial solution

fastest, locally optimal

slow, global solution

faster, locally optimal

problem definition p for translate

Lucas-Kanade

have a good intial guess p the guess p is very close to the final p

solve this

LK motion estimation is a special
case of LK image alignment

in KL alignment p is an affine transmation matrix

in LK motion estimation p is translate matrix

KLT Tracking

good feature
void smooth regions and edges

to make Hession matrix inversion the eigenvalues should be large

find corners staisfying two eigenvalues larger than threshold

the apply Lucas-Kanade method to each corner

update corner position

1. initial a position

2. compute mean-shift

3. update position

m(x) is a position of mean sample point g is guassian kernel, h is hyperparameter

for same spatial density add weights

shift: update:

target(q) and candidate(p(y)) descriptor normalized color histogram

measure the minimum distance/similarity(d(y)) = maximum cosine distance find y at that point

assuming a good inital and apply Taylor series expansion 整理之后，是mean-shift algorithm

classification

image classification

semantic segementation

instance sgemenation

object detection

Machine learning basic

supervised/unsupervised learning whether has labeled data

discrete/continuous for output

machin learning framework

given a training set of labeled examples{(xn,yn)},
estimate the prediction function f by minimizing
the prediction error on the training set

apply f to a never befor seen test example
x and output the prdiced value y=f(x)

identifying what is in the image

localizing and classigying one or more objects in an image

assigning a lable to every pixel in the iamge and treat
multiple objects of the same class as a single entity

binary label or multi-label classification

similar to semantic segementation but trading multiple
objects of the same class as distinct individual objects

definition

recognition

impage input feature representation tainable classifier ouput class label pipline

hand-crafted feature:
bag of feature

part-based models

motivation textue model

bags of words e.g. to understand rough topic about a book

1. extract local feature

2. learn “visual vocabulary”

3. quantize local features using visual vocabular

4. represent images in histogram by fequencies of “visual words”

extract descriptors from training set

clustering

trainable classifier

linear classifiers

simple to implement

decision boundaries not necessarily linear

works for any number of classes

non-parametric method

need good distance function

slow at test time

low-dimensional parametric representation

very fast at test time

cons works for two classes

K-NN select K nearest result and vote for the output

find a hyperplane that maximizes the marign between the positive and negative example

margin:2/||w||maximize margin = minize ||w||/2

maximize the margin when
classify training data correctly

for separable data

for non-separable data

maximize the margin and
minimize classification
mistakes(hinge loss function) in the result there will be some non-sperable data in 1/||w|| region

non-linear SVM map the data to higher dimensional feature space and data will be separable

non-linear SVM framwork is very powerful and flexible

find globally optimal solution

can work well with small traingin sample sizes

direct multi-class SVM achieve by combining two-class SVM instead of directly

computation, memory

Pedestrain detection

Face detection

Specify Object Model

Generate Hypotheses

Score Hypotheses

Resolve Detection

approaches

statistical template in bounding box

articulated parts model object is configuration of parts

hybrid template/parts model combine of the first two

find feature inthe box

3D-ish model

deformable 3D model

propse an alignment of the model to the image

find object parameters

approaches
sliding window

region-based proposal

mainly for gradient-based feature

rescore each propsed object based on the whole set

e.g. non-max suppressionapproaches

basic pipline

HOG&linear SVM

sytatistical template
gives score to each part and add them together

if the sum is ove a threshold, it will be a person
object model =
sum of scores of features
at fixed position

extract fixed-sized window at teach position and scale

compute HOG(histogram of gradient) feature within each window

score the window with a linear SVM classifier

perform non-maximua suppression to remove overlapping detections with lower scores resolve detection

score hypotheses

HOG feature descriptor

character for statis tical template approach

work well for non-deformable objects

fast detection

not so well for highly deformable objects

not robust to ocllusion

1. integral images for fast feature evalution

2. boosting for feature selection

3. attentional cascade for fast
non-face window rejection

以这个pixel向左上角话矩形，矩形内pixel的直累加到这个pixel的位置 fast to compute

最开始每个点的权重都相同

in each round, train one feature. Find the classifer
that achieves the lowest weight training error

for worry classified porints,
increasing their weight

after all the rounds, the final classifier as
linear combination of all wak classifier 精度与每个week classifier的权重成正比

early in the cascade, reject many negative examples, which not contain faces

chain classifiers are progressively more complex and have lower false positive rate

the detection rate and false positvie rate of the cascade 是每一级的乘积

H calls Hessian matrix

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