advancedskrald/projectproposal/projectproposal.lyx

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\begin_layout Title
AAR: Project Proposal
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\begin_layout Author
Casper V.
Kristensen - 201509411
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Alexander Munch-Hansen - 201505956
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\begin_layout Section
Envisioned Use Case
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We wish to create an augmented reality multiplayer chess experience.
Each player will be playing separately at their own chess board using their
own physical pieces, while the opponent's pieces which are not present
physically will be rendered through a see-through head mounted display
like the Hololense.
We envision the final product to look similar to this
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https://www.youtube.com/embed/Geyr95Nl8mc?start=4&end=23&autoplay=1
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project by Günther et al.
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.
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Actual Plan
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Instead of using a HoloLens, we can implement the system using a webcam
and setup similar to that of the Augmented Reality course.
We will utilise machine learning to recognise the board state from the
camera image, which, most notably, involves identifying the different board
pieces and their position on the board.
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To begin with, getting the system working from the perspective of a single
user does not require being able to separate black and white pieces, since
each player will only play using their own physical ones.
Once a method for detecting a single colour has been developed, however,
expanding the dataset to include the other one should be trivial.
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In addition to reducing the scope by colour, we will also start by implementing
an algorithm for identifying only two or three different and easily distinguish
able pieces like the pawn, knight, and king.
Expanding the algorithm to the remaining pieces may prove difficult, since,
for example, the original
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https://en.wikipedia.org/wiki/Staunton_chess_set
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pawn and bishop may look similar, in which case we will have to look into
creating our own.
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Another issue is occluding the virtual pieces with the real ones, allowing
for a seamless augmented reality experience.
To utilise our knowledge from the previous course we would hope to use
the Unity game engine for this task, however, as some OpenCV functionality
is unavailable through the OpenCVForUnity module, we may have to use other
frameworks for the object detection, and therefore also for rendering virtual
objects, considering integration and operability.
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Lastly, as shown in the video by Günther et al., showing where each piece
is allowed to move upon picking it up or pointing to it could increase
the usefulness of the system, as well as the scope of the project, if there
is time to do so.
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\begin_layout Subsection
Milestones
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Persuade lecturer or TA into letting us use cool new HoloLens rather than
boring old webcam.
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\begin_layout Enumerate
Implement board detection and homography/warping.
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\begin_layout Enumerate
Gather initial dataset for 2-3 pieces of a single colour based on previous
step and simple script.
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Implement and train machine learning algorithm for classification of pieces
in reduced dataset.
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Virtual object rendering and occlusion between real and augmented pieces.
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Expand dataset to include both black and white pieces.
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Integration with open source chess engine to show player optimal move.
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