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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. 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

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  1. Virtual Dart: An Augmented Reality Game on Mobile Device Supervisor: Professor Michael R. Lyu Prepared by: Lai Chung Sum Siu Ho Tung

  2. Outline • Background Information • Motivation • Objective • Methods • Results • Future Work • Q & A

  3. What is Augmented Reality (AR)? • A combination of real world and computer generated data • Add computer graphic into video

  4. Background Information • Most mobile phones equipped with cameras • Games written in J2ME & proprietary development platform

  5. Background Information • Typical mobile games

  6. Background Information • Mobile games employed Augmented Reality

  7. Motivation • How can the game “remember” external environment?  Save external environment information

  8. 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

  9. Problems to be solved… • What information should we store? • How does the game recognize the information? • How does the game perform motion tracking?

  10. Introduction to Mobile Video Object Tracking Engine (mVOTE) • Convert the camera movement into translational movement and degree of rotation

  11. 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

  12. What does our program need?

  13. Program Flow – Initial Algorithm

  14. Program Flow – Initial Algorithm

  15. Experiment of Feature Selection • Feature Selection in mVOTE VS FAST Corner Detection Algorithm • Testing Environment • Normal lighting • Insufficient lighting

  16. Normal Lighting Condition

  17. Normal Lighting Condition

  18. Normal Lighting Condition

  19. Insufficient Lighting Condition

  20. Insufficient Lighting Condition

  21. 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

  22. Experiment of Initial Approach

  23. Experiment of Initial Approach

  24. Experiment of Initial Approach

  25. Initial Feature Recognition Conclusion • Accuracy?? LOW!

  26. Program Flow – Enhanced Feature Recognition Algorithm

  27. Enhanced Feature Recognition

  28. Enhanced Feature Recognition

  29. Enhanced Feature Recognition

  30. 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

  31. 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

  32. 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

  33. e.g. r = 3, n = 12, barrier = 25 215 – 65 = 150 > 25 =barrier  Marked by red 65 – 39 = 26 > 25 =barrier Marked by Blue

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

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

  36. 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?)

  37. FAST Corner Detector • Advantage: • Fast • Disadvantages: • Cannot work well in noisy environment • Accuracy depends on parameter – barrier

  38. FAST Corner Detector

  39. 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

  40. 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

  41. Future Works • Allow users to load saved features • Increase the speed of feature recognition • Add physical calculation engine

  42. Q & A

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