robust object matching for persistent tracking with heterogeneous features
DESCRIPTION
Presented by Enrique G. Ortiz. Robust Object matching for persistent tracking with heterogeneous features. System Overview. Prewarping Stage. Approximately match resolutions Define vehicles three directions Chamfer matching for initial translation. Within Sequence Object Mask Generation. - PowerPoint PPT PresentationTRANSCRIPT
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ROBUST OBJECT MATCHING FOR PERSISTENT TRACKING WITH HETEROGENEOUS FEATURES
Presented by Enrique G. Ortiz
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System Overview
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Prewarping Stage
Approximately match resolutions
Define vehicles three directions
Chamfer matching for initial translation
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Within Sequence Object Mask Generation
Robust Blob Feature Extraction
Blob Tracking via Earth Mover’s Distance
Object Mask Generation
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Blob Detection
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Earth Mover’s Distance
Intuition
Transportation Problem
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Region Matching through EMDDistribution 1 Distribution 2
wi,j
wi,j[R, G, B, X, Y, A]di,j
di,j
di,j fi,j=min(wi,j,w’I,j)
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Object Mask Generation
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Segmentation Result
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Cross Sequence Quasi-Rigid Alignment
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Detect and Classify Edges
Canny Edge Detection
Classify Edges
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Descriptor and Matching
Band Image [Y R G B]
Normalized Correlation
€
γ u,v( ) =f (x,y) − f[ ]x,y
∑ t(x,y) − t[ ]
f (x,y) − f[ ]2t(x,y) − t[ ]
2
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Matching Result
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Reject Outliers
Outliers Edges with similar appearance False matches Edges with cluttered background
Solution: Impose two rigidity constraints
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Reject Outliers - Affine Rigidity Target depth variation smaller than
range Distances should satisfy 1D affine
model
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Reject Outliers – 3D Line Reconstruction Metadata
Camera Rotation
2D Line Correspondences Camera Translation 3D Line Location
Reject line with large 3D location error
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Outlier Rejection Result
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Extend Line Segments Extend until intersection Modify for equal orientation
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Interpolating Flow Fields
Similarity Transformation
Weight flow less as move away from line
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Incorporation of Points
Harris corner detector Match by correlation Alone insufficient for global motion
model
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“Quasi-Rigid” Alignment Results
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“Quasi-Rigid” Alignment Results
Query Model Warped Query
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Match path to a local distribution of patches within constraints provided by aligned images
Represent patches as oriented energy filter outputs Captures significant features Ignores some illumination effects Each patch captures spatial arrange of edge energy and
orientations within patch Perform nearest-neighbor matching with patches in target
image to account for alignment error Score of the patch is computed as that of the best
matching patch within a small range of translations around the patch
Scores from all local patches aggregated to form overall score between two images
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Matching
Oriented Energy Filter (0, 45, 90, 135)
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Patch Matching
- Spatial Edge Energy- Orientation Weighted sum of correlation score
from all the pixels
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Local Correlation Scores
Global Affine Alignment “Quasi-Rigid” Alignment
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Color and Texture
Local Template Matching Similar to Normalized Correlation
Average Color Similarity Measure the angle between RGB vectors
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Results
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Results
Performance well for sequences up to 30 seconds apart (98.8%)
Does well up to approximately 30° pose change
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2D Affine Alignment + global correlation80% for pose < 33°
Their Method91% for pose < 33°
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End