Virtual dart an augmented reality game on mobile device
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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 l.jpg

Virtual Dart: An Augmented Reality Game on Mobile Device

Supervisor: Professor Michael R. Lyu

Prepared by:

Lai Chung Sum

Siu Ho Tung

Outline l.jpg

  • Background Information

  • Motivation

  • Objective

  • Methods

  • Results

  • Future Work

  • Q & A

What is augmented reality ar l.jpg
What is Augmented Reality (AR)?

  • A combination of real world and computer generated data

  • Add computer graphic into video

Background information l.jpg
Background Information

  • Most mobile phones equipped with cameras

  • Games written in J2ME & proprietary development platform

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Background Information

  • Typical mobile games

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Background Information

  • Mobile games employed Augmented Reality

Motivation l.jpg

  • How can the game “remember” external environment?

     Save external environment information

Objectives l.jpg

  • Demonstrate how a game “remember” its external environment for Augmented Reality (AR)

  • Virtual Dart is just a game for demonstration of the proposed methodology

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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 l.jpg
Introduction to Mobile Video Object Tracking Engine (mVOTE)

  • Convert the camera movement into translational movement and degree of rotation

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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


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Experiment of Feature Selection

  • Feature Selection in mVOTE VS FAST Corner Detection Algorithm

  • Testing Environment

    • Normal lighting

    • Insufficient lighting

Analysis l.jpg

  • 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 l.jpg
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 l.jpg
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 l.jpg
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 l.jpg

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

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

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

Slide34 l.jpg

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

Slide35 l.jpg

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

Fast corner detector36 l.jpg
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 l.jpg
FAST Corner Detector

  • Advantage:

    • Fast

  • Disadvantages:

    • Cannot work well in noisy environment

    • Accuracy depends on parameter – barrier

How does feature recognition works l.jpg
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

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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

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Future Works

  • Allow users to load saved features

  • Increase the speed of feature recognition

  • Add physical calculation engine