Method

DCCN[la] [DCCN]
[Anonymous Submission]

Submitted on 7 Aug. 2017 15:36 by
[Anonymous Submission]

Running time:25 ms
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
DCCN; speed: 25ms (fast version: 20ms)
Parameters:
Optimized by Adam
Latex Bibtex:

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 95.18 % 92.44 % 94.69 % 95.68 % 2.45 % 4.32 %
UMM_ROAD 97.10 % 95.31 % 96.64 % 97.56 % 3.73 % 2.44 %
UU_ROAD 94.89 % 92.27 % 94.98 % 94.80 % 1.63 % 5.20 %
URBAN_ROAD 95.93 % 93.32 % 95.52 % 96.35 % 2.49 % 3.65 %
UM_LANE 92.70 % 90.94 % 92.39 % 93.01 % 1.35 % 6.99 %
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 99.10 % 99.08 % 98.87 % 98.79 % 98.19 % 97.38 % 96.70 % 90.70 % 89.33 %
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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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