1 / 97

PLANAR VEHICLE TRACKING USING A MONOCULAR BASED MULTIPLE CAMERA VISUAL POSITION SYSTEM

PLANAR VEHICLE TRACKING USING A MONOCULAR BASED MULTIPLE CAMERA VISUAL POSITION SYSTEM. Anthony Hinson April 22, 2003. Overview. Introduction Image Processing Primitive Statistical Planar Visual Positioning Fundamentals Application. Overview. Testing and Results Simulation Actual

eeva
Télécharger la présentation

PLANAR VEHICLE TRACKING USING A MONOCULAR BASED MULTIPLE CAMERA VISUAL POSITION SYSTEM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PLANAR VEHICLE TRACKING USING A MONOCULAR BASED MULTIPLE CAMERA VISUAL POSITION SYSTEM Anthony Hinson April 22, 2003

  2. Overview • Introduction • Image Processing • Primitive • Statistical • Planar Visual Positioning • Fundamentals • Application Center for Intelligent Machines and Robotics

  3. Overview • Testing and Results • Simulation • Actual • Conclusions • Graphical User Interface • Future Work • Surface Positioning • Time Based Models • Demonstration and Questions Center for Intelligent Machines and Robotics

  4. Statistical Image Processing Primitive Image Processing Introduction Planar Positioning Fundamentals Planar Positioning Application Testing Link Page Center for Intelligent Machines and Robotics

  5. Link Page Graphical User Interface Conclusions Future Work Center for Intelligent Machines and Robotics

  6. Introduction Concept • Simple Monocular Vision Based Position System for Tracking of Indoor and Outdoor Vehicles Center for Intelligent Machines and Robotics

  7. Introduction Concept • Uses Single or Multiple Cameras to Determine Vehicle Position and Orientation Center for Intelligent Machines and Robotics

  8. Introduction Concept • Vehicle Position and Orientation Determined Via Tracking Features On Top of the Vehicle Center for Intelligent Machines and Robotics

  9. Introduction Concept • Advantages • Well Suited forIndoor Vehicles • Accurate Position Information • Easy to Implement • Non-Intrusive to Environment or Vehicle • Not Specific to Certain Hardware • One-Time Setup Center for Intelligent Machines and Robotics

  10. Introduction Concept • Advantages • Video Feed Can Be Used for Monitoring and Positioning Simultaneously Center for Intelligent Machines and Robotics

  11. Introduction Concept • Disadvantages • Reliability is Dependent on Environmental Conditions • Accuracy Decreases with Range • Planar Positioning System (2D Only) Center for Intelligent Machines and Robotics

  12. Image Processing Primitive • Initial Image Processing Work • Some Routines Good for Basic Image Enhancement • Largely Ineffective for Feature Extraction Center for Intelligent Machines and Robotics

  13. Image Processing Primitive • ColorBias • Process – Shifts Individual Color Channel Values • Usage – Used for Hue Correction • Synopsis– Reasonably Fast and Effective Center for Intelligent Machines and Robotics ColorBias Original Image Modified Image

  14. Image Processing Primitive • ProgressiveSmooth • Process – Performs Weighted Averaging with Neighboring Pixels • Usage– Used for Noise Removal and Anti-Aliasing • Synopsis– Effective but Slow Center for Intelligent Machines and Robotics ProgressiveSmooth Original Image Modified Image

  15. Image Processing Primitive • ColorDistinguish • Process – Removes Pixels that Are not Within the User-Specified Range • Usage– Color Feature Extraction • Synopsis– Limited Functionality / No Longer Used Center for Intelligent Machines and Robotics ColorDistinguish Original Image Modified Image

  16. Image Processing Primitive • ColorRemove • Process – Removes Pixels that Are not Within the User-Specified Range • Usage– Removes Unwanted Colors • Synopsis– Limited Functionality / No Longer Used Center for Intelligent Machines and Robotics ColorRemove Original Image Modified Image

  17. Image Processing Primitive • Threshold • Process – Removes Pixels with Values Less Than User-Specified Boundary • Usage– Removes Dark Pixels / Was Typically Used to Enhance Edge Information • Synopsis– No Longer Used Center for Intelligent Machines and Robotics Threshold Original Image Modified Image

  18. Image Processing Primitive • EdgeDetect • Process – Calculates Color Discrepancy Between Adjacent Pixels • Usage– Finds Edges of Color Boundaries • Synopsis– Relatively Fast and Effective Center for Intelligent Machines and Robotics EdgeDetect Original Image Modified Image

  19. Image Processing Primitive • ScreenText • Process – Writes Alphanumeric Characters to a Video Pixel Array • Usage– Currently Used to Display Range Data in Video Stream • Synopsis– Works Very Well Center for Intelligent Machines and Robotics ScreenText Original Image Modified Image

  20. Image Processing Primitive • Primitive Image Processing Functions Insufficient for Visual Positioning • Work Reasonably Well on Simulated Images • Work Poorly on Experimental Images Center for Intelligent Machines and Robotics

  21. Image Processing Statistical • Desired Capabilities of Feature Classifier • Capable of Handling Simulated Data • Capable of Handling Experimental Data • Fast Processing Speed Center for Intelligent Machines and Robotics

  22. Image Processing Statistical • Color Space (RGB Space) • All Possible Digital Colors Represented by Cube with Dimension of 256 • Each Axis Represents Color Center for Intelligent Machines and Robotics

  23. Image Processing Statistical • In RGB Space • Color Distributions Have Physical Meaning • Distributions Can be Represented by 3D Shapes in RGB Space Center for Intelligent Machines and Robotics

  24. Image Processing Statistical • In RGB Space • Data from an Image Can be Displayed as Data Points in RGB Space Center for Intelligent Machines and Robotics

  25. Image Processing Statistical • Color Classifiers Used in This Research • Color Range • Normalized Color Direction • 3D Gaussian Color Distribution • 2D Normalized Gaussian Color Distribution Center for Intelligent Machines and Robotics

  26. Image Processing Statistical • Color Range • Basically Same as ColorDistinguish • Distribution Defined by High & Low Values for Each Color Channel Separately • Distribution is Represented by a Box in RGB Space Center for Intelligent Machines and Robotics

  27. Image Processing Statistical • Color Range • High/Low Values Determined By 1-D Gaussian Distributions for Each Color Channel • High Value = m +ns • Low Value = m – ns • Pixels Located Inside the Box are Considered to be Target Color Center for Intelligent Machines and Robotics

  28. Image Processing Statistical • Color Range • Advantages • Very Fast • Disadvantages • Not Very Precise • Typically Yields High Error Center for Intelligent Machines and Robotics

  29. Image Processing Statistical Color Range Sample Image Original Image Processed Image Center for Intelligent Machines and Robotics

  30. Image Processing Statistical • Color Range in RGB Space • Black: Correctly Classified Non-Feature pixels • White: Correctly Classified Feature Pixels • Blue: Missed Feature Pixels Center for Intelligent Machines and Robotics

  31. Image Processing Statistical • Color Direction • Searches for Pixels Using Color Vectors in RGB Space • Distribution is Defined as a Target Color and Range • Resulting Distribution Shape is a Conic Section Center for Intelligent Machines and Robotics

  32. Image Processing Statistical • Color Direction • Color Normalization Equations • Converts Discreet Color Value to Normalized Color Direction Vector Center for Intelligent Machines and Robotics

  33. Image Processing Statistical • Color Direction • Distribution Defined By: • Target Color (Mean of Normalized Feature Pixels) Center for Intelligent Machines and Robotics

  34. Image Processing Statistical • Color Direction • Distribution Defined By: • Color Direction Variance (Each Color Separate) Center for Intelligent Machines and Robotics

  35. Image Processing Statistical • Color Direction • Distribution Defined By: • Any Pixel with a Color Direction Between m +ns and m – ns is Considered to be Feature Pixel Center for Intelligent Machines and Robotics

  36. Image Processing Statistical • Color Direction • Advantages • Discards Brightness Information • Can Find Colors in the Light or Shadows • Inherently Compensates for Scattered Color Data • Disadvantages • More Likely to Have False Hits on Similar Colored Objects in Scene Center for Intelligent Machines and Robotics

  37. Image Processing Statistical Color Direction Sample Image Original Image Processed Image Center for Intelligent Machines and Robotics

  38. Image Processing Statistical • Color Direction RGB Space • Black: Correctly Classified Non-Feature pixels • White: Correctly Classified Feature Pixels • Blue: Missed Feature Pixels • Red: False Hit Pixels Center for Intelligent Machines and Robotics

  39. Image Processing Statistical • 3D Gaussian Distribution • Classifies Data According to a Normal Distribution • Classifier is Represented by a 3D Ellipsoid in RGB Space Center for Intelligent Machines and Robotics

  40. Image Processing Statistical • 3D Gaussian Distribution • Classifier’s Shape and Position are Defined By: • Mean Color of Feature Data • Variance Within Each Color Channel • Covariance Between Color Channel Center for Intelligent Machines and Robotics

  41. Image Processing Statistical • 3D Gaussian Distribution • Probability Density Function (PDF) • Where Center for Intelligent Machines and Robotics

  42. Image Processing Statistical • 3D Gaussian Distribution • Variance Calculations Center for Intelligent Machines and Robotics

  43. Image Processing Statistical • 3D Gaussian Distribution • Exponential Part of PDF Can be Used to Assess Membership of Pixel to the Distribution • r is known as Mahalanobis Distance Center for Intelligent Machines and Robotics

  44. Image Processing Statistical • 3D Gaussian Distribution • Mahalanobis Distance • The Number of Standard Deviations The Current Pixel is from the Mean • Any Pixel with an r of Less Than User-Specified Value is Considered Member of Distribution Center for Intelligent Machines and Robotics

  45. Image Processing Statistical • 3D Gaussian Distribution • Advantages • Very Accurate for Most Distributions • Compensates for Data Clusters at Any Location and Orientation in RGB Space • Disadvantages • Color Distribution Must Be Relatively Gaussian in Distribution Center for Intelligent Machines and Robotics

  46. Image Processing Statistical 3D Gaussian Distribution Sample Image Original Image Processed Image Center for Intelligent Machines and Robotics

  47. Image Processing Statistical • 3D Gaussian RGB Space • Black: Correctly Classified Non-Feature pixels • White: Correctly Classified Feature Pixels • Blue: Missed Feature Pixels • Red: False Hit Pixels Center for Intelligent Machines and Robotics

  48. Image Processing Statistical • 2D Normalized Gaussian Distribution • Hybrid of 3D Gaussian and Color Direction • Converts 3D Color Cube to 2D Color Triangle Center for Intelligent Machines and Robotics

  49. Image Processing Statistical • 2D Normalized Gaussian Distribution • Color Data Reduced to 2 Dimensions • Removes Brightness Information • Bivariate Gaussian Classifier • Classifier Shape is an Ellipse within the Color Triangle Center for Intelligent Machines and Robotics

  50. Image Processing Statistical • 2D Normalized Gaussian Distribution • Color Data Flattening (Convert RGB Coordinates to XY Coordinates) Center for Intelligent Machines and Robotics

More Related