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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\begin_layout Section
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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\begin_layout Enumerate
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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\begin_layout Enumerate
Implement and train machine learning algorithm for classification of pieces
 in reduced dataset.
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\begin_layout Enumerate
Virtual object rendering and occlusion between real and augmented pieces.
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\begin_layout Enumerate
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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