272 lines
6.3 KiB
Plaintext
272 lines
6.3 KiB
Plaintext
#LyX 2.3 created this file. For more info see http://www.lyx.org/
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\end_header
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\begin_body
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\begin_layout Title
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AAR: Project Proposal
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\end_layout
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\begin_layout Author
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Casper V.
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Kristensen - 201509411
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\begin_inset Newline newline
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\end_inset
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Alexander Munch-Hansen - 201505956
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\end_layout
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\begin_layout Section
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Envisioned Use Case
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\end_layout
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\begin_layout Standard
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We wish to create an augmented reality multiplayer chess experience.
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Each player will be playing separately at their own chess board using their
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own physical pieces, while the opponent's pieces – which are not present
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physically – will be rendered through a see-through head mounted display
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like the Hololense.
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We envision the final product to look similar to this
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\begin_inset Foot
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status open
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\begin_layout Plain Layout
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\begin_inset Flex URL
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status open
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\begin_layout Plain Layout
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https://www.youtube.com/embed/Geyr95Nl8mc?start=4&end=23&autoplay=1
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\end_layout
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\end_inset
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\end_layout
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\end_inset
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project by Günther et al.
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\begin_inset CommandInset citation
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LatexCommand cite
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key "Gunther:2018:CET:3170427.3188647"
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literal "false"
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\end_inset
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.
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\end_layout
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\begin_layout Section
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Actual Plan
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\end_layout
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\begin_layout Standard
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Instead of using a HoloLens, we can implement the system using a webcam
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and setup similar to that of the Augmented Reality course.
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We will utilise machine learning to recognise the board state from the
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camera image, which, most notably, involves identifying the different board
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pieces and their position on the board.
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\end_layout
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\begin_layout Standard
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To begin with, getting the system working from the perspective of a single
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user does not require being able to separate black and white pieces, since
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each player will only play using their own physical ones.
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Once a method for detecting a single colour has been developed, however,
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expanding the dataset to include the other one should be trivial.
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\end_layout
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\begin_layout Standard
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In addition to reducing the scope by colour, we will also start by implementing
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an algorithm for identifying only two or three different and easily distinguish
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able pieces like the pawn, knight, and king.
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Expanding the algorithm to the remaining pieces may prove difficult, since,
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for example, the original
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\begin_inset Foot
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status collapsed
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\begin_layout Plain Layout
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https://en.wikipedia.org/wiki/Staunton_chess_set
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\end_layout
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\end_inset
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pawn and bishop may look similar, in which case we will have to look into
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creating our own.
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\end_layout
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\begin_layout Standard
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Another issue is occluding the virtual pieces with the real ones, allowing
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for a seamless augmented reality experience.
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To utilise our knowledge from the previous course we would hope to use
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the Unity game engine for this task, however, as some OpenCV functionality
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is unavailable through the OpenCVForUnity module, we may have to use other
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frameworks for the object detection, and therefore also for rendering virtual
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objects, considering integration and operability.
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\end_layout
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\begin_layout Standard
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Lastly, as shown in the video by Günther et al., showing where each piece
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is allowed to move upon picking it up or pointing to it could increase
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the usefulness of the system, as well as the scope of the project, if there
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is time to do so.
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\end_layout
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\begin_layout Subsection
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Milestones
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\end_layout
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\begin_layout Enumerate
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Persuade lecturer or TA into letting us use cool new HoloLens rather than
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boring old webcam.
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\end_layout
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\begin_layout Enumerate
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Implement board detection and homography/warping.
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\end_layout
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\begin_layout Enumerate
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Gather initial dataset for 2-3 pieces of a single colour based on previous
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step and simple script.
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\end_layout
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\begin_layout Enumerate
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Implement and train machine learning algorithm for classification of pieces
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in reduced dataset.
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\end_layout
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\begin_layout Enumerate
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Virtual object rendering and occlusion between real and augmented pieces.
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\end_layout
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\begin_layout Enumerate
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Integration with open source chess engine to show player optimal move.
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\end_layout
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\begin_layout Enumerate
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Expand
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\end_layout
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\begin_layout Enumerate
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Expand dataset to include both black and white pieces.
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\end_layout
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