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Virtual Dart: An Augmented Reality Game on Mobile Device. Supervisor: Professor Michael R. Lyu. Prepared by: Lai Chung Sum Siu Ho Tung. Outline. Background Information Motivation Objective Methods Results Future Work Q & A. What is Augmented Reality (AR)?.

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virtual dart an augmented reality game on mobile device

Virtual Dart: An Augmented Reality Game on Mobile Device

Supervisor: Professor Michael R. Lyu

Prepared by:

Lai Chung Sum

Siu Ho Tung

outline
Outline
  • Background Information
  • Motivation
  • Objective
  • Methods
  • Results
  • Future Work
  • Q & A
what is augmented reality ar
What is Augmented Reality (AR)?
  • A combination of real world and computer generated data
  • Add computer graphic into video
background information
Background Information
  • Most mobile phones equipped with cameras
  • Games written in J2ME & proprietary development platform
background information5
Background Information
  • Typical mobile games
background information6
Background Information
  • Mobile games employed Augmented Reality
motivation
Motivation
  • How can the game “remember” external environment?

 Save external environment information

objectives
Objectives
  • Demonstrate how a game “remember” its external environment for Augmented Reality (AR)
  • Virtual Dart is just a game for demonstration of the proposed methodology
problems to be solved
Problems to be solved…
  • What information should we store?
  • How does the game recognize the information?
  • How does the game perform motion tracking?
introduction to mobile video object tracking engine mvote
Introduction to Mobile Video Object Tracking Engine (mVOTE)
  • Convert the camera movement into translational movement and degree of rotation
what is a feature
What is a feature?
  • Section of an image that is easily highlighted for the purpose of detection and tracking
  • Have a high contrast in relation to its immediate surroundings

X

experiment of feature selection
Experiment of Feature Selection
  • Feature Selection in mVOTE VS FAST Corner Detection Algorithm
  • Testing Environment
    • Normal lighting
    • Insufficient lighting
analysis
Analysis
  • Normal Lighting

 Both algorithms worked reasonably well

  • Insufficient Lighting

 Only mVOTE’s Feature Selection could produce output

  • Occasionally, Feature Selection in mVOTE selected some flat regions as features
  • FAST Corner worked better in terms of accuracy
algorithms comparison
Algorithms Comparison
  • Initial Feature Recognition VS Enhanced Feature Recognition
    • Initial Approach: 3 Features
    • New Approach: Whole selection area
  • Reason for LOW accuracy: (Initial Approach)

 Features may not be descriptive enough

improvement of feature selection
Improvement of Feature Selection
  • Two conditions of a “Good” Feature:
    • Descriptive
    • Large internal intensity difference
  • Corner Detector can help us to find out good features
fast corner detector
FAST Corner Detector
  • Examine a small patch of image
  • Considering the Bresenham Circle of radius r around the candidate pixel which is called p
  • Intensities of n continuous pixels on the circle are larger than p or smaller than p by barrier

 Potential corner

slide33
e.g. r = 3, n = 12, barrier = 25

215 – 65 = 150 > 25 =barrier  Marked by red

65 – 39 = 26 > 25 =barrier Marked by Blue

fast corner detector36
FAST Corner Detector
  • The typical values of r and n are 3 and 12 respectively
  • For the value of barrier, we did an experiment to choose the value
  • We chose “25” after the experiment (for what?)
fast corner detector37
FAST Corner Detector
  • Advantage:
    • Fast
  • Disadvantages:
    • Cannot work well in noisy environment
    • Accuracy depends on parameter – barrier
how does feature recognition works
How does Feature Recognition works?
  • Full screen as search window
  • Use Sum Square Difference (SSD) to calculate the similarity of blocks
  • Still slow in current stage (~20 – 60sec)
  • Tried to use a smaller image and scale up to full screen
    • Scaling step is too time consuming
motion tracking during the game
Motion Tracking during the game
  • Keep track of three features
  • Use two features to locate dart board
  • The last feature point is used for backup
  • Use if either one of the feature points fail
  • Condition for a feature point failure
    • Feature point is at the edge of the screen
    • Two feature points are too close
future works
Future Works
  • Allow users to load saved features
  • Increase the speed of feature recognition
  • Add physical calculation engine
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