A Robust Method for Lane Tracking Using RANSAC James Ian Vaughn Daniel Gicklhorn CS664 Computer...
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Transcript of A Robust Method for Lane Tracking Using RANSAC James Ian Vaughn Daniel Gicklhorn CS664 Computer...
A Robust Method for Lane Tracking Using RANSAC
James Ian VaughnDaniel Gicklhorn
CS664 Computer VisionCornell University
Spring 2008
Original Project Plan
• Break into slices to both allow for non-linear solutions as wellas reducing time for line fitting.
• Detect predominant edgemarker or road edgeif no markers present
• Algorithm must be robust enough to handle when markingsare not present or badly defined.
Project Plan
Road Model Assumptions
• Road edges are strong
• Road edges have a gradient similar to the model edge
• Road edges are consistent between frames
• Roads consist of several segments of approximately linear edge points
• Actual edges have a lot of inlier points
Model Assumptions
RANSAC as a Line Fitting Algorithm
• Fast efficient line fitting
• Dynamic selection ofcandidate lines withtunable error constraint
• Able to incorporate spatialpriors and progressivemodel fitting into constraint.
• Can detect predominant road edges along side if lanemarkers aren’t present.
RANSAC
Implementation
• Using RANSAC, we can incorporatea progressive spatial prior intothe error function.
• Each frame is broken in to road“slices”, consistent, horizontal sections most likely to contain road markings.
• This reduces the amount of time needed for RANSAC as well asproducing a non-linear output
Implementation
Candidate Error Criteria
• The RANAC error function is the heart of our method.
• The error considers both local andpriori models to locate “good”line candidates in the scene
• Lines are restricted by slope,distance from a priori “model”line, and by the edge intensity,density, and gradient angle oftheir potential “inlier” members.
RANSAC Error Model
Line End 1
Line End 2
Distance 1
Distance 2
Threshold
Dis
tanc
e Fr
om T
est
Mod
el
Model Line
Test Line
angle
AngleMag
natu
de
angl
e
Test Point
Candidate Error Criteria
RANSAC Error Model
Line End 1
Line End 2
Distance 1
Distance 2
Threshold
Dis
tanc
e Fr
om T
est
Mod
el
Model Line
Test Line
angle
AngleMag
natu
de
angl
e
Test Point
Error is a linear combination of:• Scaled distance of candidate line to model line• Angular difference between candidate and
model lines.• Number of consensus points to a given candidate.• Gradient angle difference between local points
to a candidate line and the model line.• Density of consensus points to a candidate line.• Gradient magnitudes of local points to a candidate line.
Candidate Error Criteria
RANSAC Error Model
Line End 1
Line End 2
Distance 1
Distance 2
Threshold
Dis
tanc
e Fr
om T
est
Mod
el
Model Line
Test Line
angle
AngleMag
natu
de
angl
e
Test Point
Error is a linear combination of:• Scaled distance of candidate line to model line
- Maintains spatial / temporal consistency.• Angular difference between candidate and
model lines.
- Maintains spatial /temporal angular consistency.
• Number of consensus points to a given candidate.
- Looks for best probability of a good line.• Gradient angle difference between local points to a
candidate line and the model line.
- Looks for consistent gradients to model line.• Density of consensus points to a candidate line.
- Prefers denser line edges to lines.• Gradient magnitudes of local points to a candidate line.
- Prefers most pronounced lines.
Results
Simulation Results• Results show excellent marking
detection and outlier tolerance.
• With proper calibration andweighting, model performswell.
• Outputs show need fortemporal filtering, butoutliers are withintolerance levels.
Results
Videos
Conclusions
Conclusions and Future work• RANSAC is an easy to constrain / almost modular method
for line detection and does well in scaling to the scene where others can fail.
• Future work to improve this model would include• Better optical flow• Robust calibration• Optimal parameter tuning• Temporal Filtering• Vehicle Sensor Integration