planar vehicle tracking using a monocular based multiple camera visual position system anthony...
TRANSCRIPT
PLANAR VEHICLE TRACKING USING A MONOCULAR BASED MULTIPLE
CAMERA VISUAL POSITION SYSTEM
Anthony HinsonApril 22, 2003
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Overview
• Introduction• Image Processing
– Primitive– Statistical
• Planar Visual Positioning – Fundamentals– Application
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Overview
• Testing and Results– Simulation– Actual
• Conclusions• Graphical User Interface• Future Work
– Surface Positioning– Time Based Models
• Demonstration and Questions
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Link Page
IntroductionPrimitive Image
ProcessingStatistical Image
Processing
TestingPlanar Positioning
FundamentalsPlanar Positioning
Application
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Link Page
Conclusions Future WorkGraphical User
Interface
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Introduction
• Simple Monocular Vision Based Position System for Tracking of Indoor and Outdoor Vehicles
Concept
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Introduction
• Uses Single or Multiple Cameras to Determine Vehicle Position and Orientation
Concept
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Introduction
• Vehicle Position and Orientation Determined Via Tracking Features On Top of the Vehicle
Concept
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Introduction
• Advantages– Well Suited for Indoor Vehicles– Accurate Position Information– Easy to Implement– Non-Intrusive to Environment or Vehicle– Not Specific to Certain Hardware– One-Time Setup
Concept
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Introduction
• Advantages– Video Feed Can
Be Used for Monitoring and Positioning Simultaneously
Concept
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Introduction
• Disadvantages– Reliability is Dependent on Environmental
Conditions– Accuracy Decreases with Range– Planar Positioning System (2D Only)
Concept
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Image Processing
• Initial Image Processing Work– Some Routines Good for Basic Image
Enhancement– Largely Ineffective for Feature Extraction
Primitive
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Image Processing
• ColorBias– Process – Shifts Individual Color Channel Values– Usage – Used for Hue Correction – Synopsis – Reasonably Fast and Effective
Primitive
Modified ImageOriginal Image
ColorBias
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Image Processing
• ProgressiveSmooth– Process – Performs Weighted Averaging with
Neighboring Pixels– Usage – Used for Noise Removal and Anti-Aliasing– Synopsis – Effective but Slow
Primitive
Modified ImageOriginal Image
ProgressiveSmooth
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Image Processing
• ColorDistinguish– Process – Removes Pixels that Are not Within the
User-Specified Range – Usage – Color Feature Extraction– Synopsis – Limited Functionality / No Longer Used
Primitive
Modified ImageOriginal Image
ColorDistinguish
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Image Processing
• ColorRemove– Process – Removes Pixels that Are not Within the
User-Specified Range – Usage – Removes Unwanted Colors– Synopsis – Limited Functionality / No Longer Used
Primitive
Modified ImageOriginal Image
ColorRemove
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Image Processing
• 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
Primitive
Modified ImageOriginal Image
Threshold
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Image Processing
• EdgeDetect– Process – Calculates Color Discrepancy Between
Adjacent Pixels – Usage – Finds Edges of Color Boundaries– Synopsis – Relatively Fast and Effective
Primitive
Modified ImageOriginal Image
EdgeDetect
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Image Processing
• ScreenText– Process – Writes Alphanumeric Characters to a Video
Pixel Array– Usage – Currently Used to Display Range Data in
Video Stream– Synopsis – Works Very Well
Primitive
Modified ImageOriginal Image
ScreenText
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Image Processing
• Primitive Image Processing Functions Insufficient for Visual Positioning – Work Reasonably Well on Simulated Images– Work Poorly on Experimental Images
Primitive
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Image Processing
• Desired Capabilities of Feature Classifier– Capable of Handling Simulated Data– Capable of Handling Experimental Data– Fast Processing Speed
Statistical
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Image Processing
• Color Space (RGB Space)– All Possible
Digital Colors Represented by Cube with Dimension of 256
– Each Axis Represents Color
Statistical
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Image Processing
• In RGB Space– Color Distributions Have Physical Meaning– Distributions Can be Represented by 3D
Shapes in RGB Space
Statistical
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Image Processing
• In RGB Space– Data from an
Image Can be Displayed as Data Points in RGB Space
Statistical
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Image Processing
• Color Classifiers Used in This Research– Color Range– Normalized Color Direction– 3D Gaussian Color Distribution– 2D Normalized Gaussian Color Distribution
Statistical
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Image Processing
• 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
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Image Processing
• Color Range– High/Low Values Determined
By 1-D Gaussian Distributions for Each Color Channel
• High Value = + n• Low Value = – n
– Pixels Located Inside the Box are Considered to be Target Color
Statistical
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Image Processing
• Color Range– Advantages
• Very Fast
– Disadvantages• Not Very Precise• Typically Yields High Error
Statistical
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Image Processing
Color Range Sample Image
Statistical
Processed ImageOriginal Image
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Image Processing
• Color Range in RGB Space– Black: Correctly
Classified Non-Feature pixels
– White: Correctly Classified Feature Pixels
– Blue: Missed Feature Pixels
Statistical
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Image Processing
• 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
Statistical
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Image Processing
• Color Direction– Color Normalization Equations
• Converts Discreet Color Value to Normalized Color Direction Vector
Statistical
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Image Processing
• Color Direction– Distribution Defined By:
• Target Color (Mean of Normalized Feature Pixels)
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Image Processing
• Color Direction– Distribution Defined By:
• Color Direction Variance (Each Color Separate)
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Image Processing
• Color Direction– Distribution Defined By:
• Any Pixel with a Color Direction Between + nand – nis Considered to be Feature Pixel
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Image Processing
• 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
Statistical
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Image Processing
Color Direction Sample Image
Statistical
Processed ImageOriginal Image
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Image Processing
• Color Direction RGB Space – Black: Correctly
Classified Non-Feature pixels
– White: Correctly Classified Feature Pixels
– Blue: Missed Feature Pixels
– Red: False Hit Pixels
Statistical
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Image Processing
• 3D Gaussian Distribution– Classifies Data
According to a Normal Distribution
– Classifier is Represented by a 3D Ellipsoid in RGB Space
Statistical
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Image Processing
• 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
Statistical
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Image Processing
• 3D Gaussian Distribution– Probability Density Function (PDF)
– Where
Statistical
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Image Processing
• 3D Gaussian Distribution– Variance Calculations
Statistical
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Image Processing
• 3D Gaussian Distribution– Exponential Part of PDF Can be Used to
Assess Membership of Pixel to the Distribution
– r is known as Mahalanobis Distance
Statistical
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Image Processing
• 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
Statistical
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Image Processing
• 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
Statistical
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Image Processing
3D Gaussian Distribution Sample Image
Statistical
Processed ImageOriginal Image
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Image Processing
• 3D Gaussian RGB Space – Black: Correctly
Classified Non-Feature pixels
– White: Correctly Classified Feature Pixels
– Blue: Missed Feature Pixels
– Red: False Hit Pixels
Statistical
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Image Processing
• 2D Normalized Gaussian Distribution– Hybrid of 3D
Gaussian and Color Direction
– Converts 3D Color Cube to 2D Color Triangle
Statistical
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Image Processing
• 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
Statistical
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Image Processing
• 2D Normalized Gaussian Distribution– Color Data Flattening (Convert RGB
Coordinates to XY Coordinates)
Statistical
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Image Processing
• 2D Normalized Gaussian Distribution– Multivariate Distribution
– Where
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Image Processing
• 2D Normalized Gaussian Distribution– Advantages
• Same As Color Direction Classifier • Allows for Better Classification Than Color
Direction
– Disadvantages• Same As Color Direction Classifier • Slower Than Color Direction
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Image Processing
2D Normalized Gaussian Distribution Sample Image
Statistical
Processed ImageOriginal Image
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Slide 54 of 97
Image Processing
• RGB Space 2D Normalized Gaussian Distribution– Black: Correctly
Classified Non-Feature pixels
– White: Correctly Classified Feature Pixels
– Blue: Missed Feature Pixels
– Red: False Hit Pixels
Statistical
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Slide 55 of 97
Planar Positioning
• Planar Positioning Concepts– Camera View Compresses 3D View to 2D– Each Pixel Represents a Vector to an Object
in Space– Distance to the Object is Unknown– Point at Where Pixel Vector Intersects Object
in Space Must be Found
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Planar Positioning
• Planar Positioning Concepts– Intersection Can be Found if Pixel Vector
Intersects a Plane– Each Pixel Will Represent a Finite Area on
the Plane
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Planar Positioning
• Quantities Needed For Reconstruction of 3D Data– Extrinsic Camera Properties
• X, Y, and Z Coordinates of Camera• Pan, Tilt, and Slant Angles of Camera
– Intrinsic Camera Properties• Field of View in Horizontal and Vertical
– Video Capture Device Properties• Resolution of Video Capture
– Planar Properties• Coordinates of Plane (D;A,B,C)
Concepts
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Planar Positioning
• Determining Required Input Data– Tracking Plane Must be Defined (Typically Parallel or
Coincident with Ground)– Camera Must be Placed in Position to Be Able to See
Plane– Video Capture Hardware Must be Initialized to
Determine Capture Resolution
Procedure
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Planar Positioning
Creating Tracking Data Lookup Table (LUT)
Procedure
• Define Pixel Grid– Camera Placed at
Home Location– Image Plane
Assumed to be at Unit Distance from Origin in Y-Direction
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Planar Positioning
Creating Tracking Data Lookup Table (LUT)
Procedure
• Define Pixel Grid– Image Plane
Boundaries Determined Trigonometrically Using Fields of View in Horizontal and Vertical
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Planar Positioning
Creating Tracking Data Lookup Table (LUT)
Procedure
• Define Pixel Grid– Image Plane Area
Divided Into Pixel Grid Corresponding to Capture Resolution
– Intersections of Gridlines are Referred to as Pixel Grid Nodes
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Planar Positioning
Creating Tracking Data Lookup Table (LUT)
Procedure
• Define Pixel Grid– Pixel Grid Node
Locations are Recorded in Homogenous Coordinates Format
(w;x,y,z)
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Planar Positioning
• Translate & Rotate Pixel Grid – Pixel Grid Points
Translated to Camera XYZ Location By Multiplying Each Point by Translation Matrix
Procedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Translate & Rotate Pixel Grid – Pixel Grid Points
Rotated to Camera Orientation By Multiplying Each Point by Three Rotation Matrices
Procedure
Creating Tracking Data Lookup Table (LUT)
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• Translation and Rotation Matrices
Planar PositioningProcedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Create Pixel Node Vectors – Vectors Created
Between Focal Point of Camera and Pixel Grid Nodes
Procedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Create Pixel Node Vectors – Vectors Represented
in Terms of Plücker Line Coordinates
or
Procedure
Creating Tracking Data Lookup Table (LUT)
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Slide 68 of 97
Planar Positioning
• Create Planar Intersection Points– Intersection Between
All Vectors and Plane Can be Found
Procedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Projective Geometry – Intersection of Line and Plane Determine a Point
Equation of Line Equation of Plane
Intersection of Line and Plane=
Procedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Determine Pixel Areas– Each Pixel Node
Intersection Point Corresponds to the Corner of a Pixel Area
Procedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Calculate Pixel Centroids– Pixel Centroid is the
Average of the Four Corners of Pixel Area
– The Centroid Represents the Coordinates that the Pixel Represents in Space
Procedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Calculate Pixel Centroids– Error Represented by
Maximum Distance from Centroid to Area Vertex
Procedure
Creating Tracking Data Lookup Table (LUT)
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Planar Positioning
• Environment Setup for Planar Positioning– Vehicle Drive Path Must be Planar– Cameras Must Cover All Drive Areas
Application
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Planar Positioning
• Vehicle Setup for Planar Positioning– Vehicle Must Have 2 Tracking Features
Distinguishable from Rest of Image Residing in a Plane Parallel to Ground
Application
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Planar Positioning
• Setup for Planar Positioning– Camera Properties Must be Precisely Defined
• Intrinsic• Extrinsic
– Environment Must be Accurately Mapped• Boundaries • Obstacles
– Tracking Plane Must be Defined as the Plane the Tracking Features are in
Application
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Planar Positioning
• Using Planar Positioning– Tracking
Information is Displayed for Allowed Areas
Application
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Testing & Results
• Test #1: Simulated Warehouse
Test #1
– Three Camera Views• Camera2• Camera3• Camera6
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Testing & ResultsTest #1
• Initial Test– Gridline
Match up• Check to See
if Grid Lines Up With Walls
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Testing & Results
• Test #1: Simulated Warehouse
Test #1
– Initial Test• Gridline
Match up
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Testing & Results
• Results– Red:
Measured– Green:
Camera2– Blue:
Camera3– Magenta:
Camera6
Test #1
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Testing & ResultsTest #1
• Results– Error Typically Less Than 1%– Some Feature Classifier Break-Down at Far Distances
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Testing & Results
• Test #2: Desktop Rover
Test #2
– Miniature Remote Control Tank-like Vehicle
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Testing & ResultsTest #2
• Test Setup– 2 Cameras– Poster board
grid • 4x4 Major
Gridlines• 1x1 Minor
Gridlines
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Testing & ResultsTest #2
• Initial Gridline Test– Software Gridlines
Overlay Match Existing Gridlines Well
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Testing & ResultsTest #2
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Testing & ResultsTest #2
• Results– Blue:
Camcorder– Magenta:
Sony CCD– Yellow:
Calculated Position
– Red: Measured Position
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Testing & ResultsTest #2
• Results– 1% to 2% Error Typically– Slightly Higher Error from Sony CCD Camera at More
Distant Locations– Vehicle Location Lost Occasionally from Camcorder
Video
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Testing & ResultsTest #3
• Test #3: Remote Controlled Truck– Inexpensive
Radio Controlled Truck
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Testing & ResultsTest #3
• Test Setup– 3 Camera Test
• Panasonic Camcorder• Sony XC711 Industrial Camera• X10 Wireless Camera (Onboard)
– Vehicle Tested on Tiled Floor Space• 8x8 Inch Floor Tiles Used as External Reference
Point for Analyzing Tracking Data
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Testing & ResultsTest #3
• Initial Gridline Test– Gridlines
Match Tile Grid Well
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Testing & ResultsTest #2
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Testing & ResultsTest #3
• Results– Blue:
Camcorder– Magenta:
Sony CCD– Yellow:
Calculate Position
– Red: Measured Position
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Testing & ResultsTest #3
• Results– Significant Classifier Breakdown with Distance or
Lighting Changes– Problematic Camera Model for Sony CCD Camera– Data from Sony CCD Camera Stayed Within 3% Error
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Testing & ResultsTest #4
• Test #4: Warehouse Test– Lighting
Conditions Very Poor
– Feature Color Information Washed Out
– Test had to be Discarded
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Conclusions
• Planar Visual Position System Works Well When:– Vehicle and Environment are Measured Well– Camera Properties are Known – Classifiers are Well-Defined
• Classification Technique Needs to Be Improved – Works Well with Simulations and Controlled
Environments– Classifier Breaks Down when Conditions Become Bad
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Future Work
• Adapt Live Video Capabilities• Surface Positioning
– Extend to Non-Planar Surfaces
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Questions & Demo
Questions ?