Method

Bayes Model Based on Normal Vector [st] [BNV]
[Anonymous Submission]

Submitted on 11 Apr. 2016 08:54 by
[Anonymous Submission]

Running time:3 s
Environment:2 cores @ 2.5 Ghz (C/C++)

Method Description:
Sparse sampling and fast computing curb probability
by plan normal vector from dense stereo. Then using
color feature and local structure feature to judge road
probability, Bayes framework could provide the
confidence level of sampling points. Finally, the SVR
method fits curb curves according to the correct
sampling points.
Parameters:
gamma1 = 0.00002;
gamma2 = 0.0002.
Latex Bibtex:

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 89.42 % 83.13 % 88.31 % 90.55 % 5.46 % 9.45 %
UMM_ROAD 92.21 % 87.99 % 91.55 % 92.89 % 9.43 % 7.11 %
UU_ROAD 85.46 % 74.07 % 85.06 % 85.86 % 4.91 % 14.14 %
URBAN_ROAD 89.75 % 84.15 % 89.02 % 90.49 % 6.15 % 9.51 %
This table as LaTeX

Behavior Evaluation


This table as LaTeX

Road/Lane Detection

The following plots show precision/recall curves for the bird's eye view evaluation.


Distance-dependent Behavior Evaluation

The following plots show the F1 score/Precision/Hitrate with respect to the longitudinal distance which has been used for evaluation.


Visualization of Results

The following images illustrate the performance of the method qualitatively on a couple of test images. We first show results in the perspective image, followed by evaluation in bird's eye view. Here, red denotes false negatives, blue areas correspond to false positives and green represents true positives.



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