Method

Road Detection Belief Network [RDBN]
[Anonymous Submission]

Submitted on 11 Nov. 2016 05:29 by
[Anonymous Submission]

Running time:0.25 s
Environment:GPU @ 2.5 Ghz (Matlab + C/C++)

Method Description:
Convolutional parsing network based on a belief network
model.
Parameters:
lr:1e-8; epoch: 10k
Latex Bibtex:

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 93.90 % 91.23 % 94.13 % 93.67 % 2.66 % 6.33 %
UMM_ROAD 95.24 % 93.78 % 96.01 % 94.48 % 4.31 % 5.52 %
UU_ROAD 92.51 % 89.75 % 92.95 % 92.08 % 2.28 % 7.92 %
URBAN_ROAD 94.15 % 91.74 % 94.88 % 93.44 % 2.78 % 6.56 %
UM_LANE 91.55 % 81.62 % 94.27 % 88.98 % 0.95 % 11.02 %
This table as LaTeX

Behavior Evaluation


Benchmark PRE-20 F1-20 HR-20 PRE-30 F1-30 HR-30 PRE-40 F1-40 HR-40
UM_LANE 99.19 % 99.32 % 99.21 % 98.05 % 96.48 % 95.07 % 94.51 % 84.88 % 77.63 %
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Road/Lane Detection

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



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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.



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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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