Meanwhile I've been drawing the skeleton of my thesis, and this is my first draft.
1 –
Introduction
Introducing
the problem, motivations and objectives of the present work.
1.1
– Problems
1.2 – State of
the Art
Brief explanation of
some methods already developed for human detection, possibly
referring to any real application that might already be implemented
(not sure if any). Also a brief overview of the evolution of
visual object detection algorithms in general.
1.3 – Solution
State my
approach to solve the problem and why it was chosen, rather than any
other.
2 –
Experimental setup
Detailed
explanation of the experimental platform implemented in ROS for the
development of the present work. Also stating and explaining the main
software tools used for elaborating the code (openCV). Possibly bring
out that this application is to be implemented in the ATLAScar thus
ilustrating the setup in run-time. This chapter will probably be
divided in sub-topics.
3 –
Integral Channel Features
A compact explanation
of the algorithm.
3.1
– Channels
What is
a channel of an image, which were computed and how
3.2
– Integral Images
What an
integral image is, what they are for, how they are computed, why they
are useful for this work.
3.3
– Features
What is
a feature, how they are computed, how many and why. Ilustration of
the random mechanism constructed for obtaining random parameters for
feature harvesting.
3.4
– “The whole picture” (not sure of the name yet, but seems to
me an important sub-topic)
An
explanation of the architecture of the code, meaning, how the image
is being treated, probably a fluxogram of some sort will come in
handy.
4
– Machine Learning Method
Brief
explanation of what a ML method is, why it is absolutely necessary
for these detection problems.
4.1
– Adaboost
What is
adaboost, why is it ideal for the present work
4.2
– Training a classifier
Explain
all the steps necessary for successfully training a classifier.
5 –
Experiments and Results
Explain how the results were
acquired, and what makes this method a valid confirmation of the
results.
5.1
– Results
Show
results.
6 –
Conclusions and Future Work
The
title explains it self.
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