## Method

End-to-End Learning with Residual Scheme for Road Segmentation [RSNet-]
[Anonymous Submission]

Submitted on 16 May. 2017 13:03 by
[Anonymous Submission]

 Running time: 0.07 s Environment: GPU @ 2.5 Ghz (Python)

 Method Description: End-to-End Learning with Residual Scheme Parameters: TBA Latex Bibtex: TBA

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 94.84 % 92.83 % 94.32 % 95.37 % 2.62 % 4.63 % UMM_ROAD 96.40 % 95.34 % 96.59 % 96.22 % 3.74 % 3.78 % UU_ROAD 93.90 % 91.69 % 92.94 % 94.88 % 2.35 % 5.12 % URBAN_ROAD 95.29 % 93.37 % 94.96 % 95.62 % 2.80 % 4.38 %
This table as LaTeX

## Behavior Evaluation

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.

## 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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