## Method

RGB36-Super [RGB36-Super]

Submitted on 28 Nov. 2019 08:36 by
Luca Caltagirone (Chalmers University of Technology)

 Running time: 0.1 s Environment: 1 core @ 2.5 Ghz (C/C++)

 Method Description: TBA Parameters: TBA Latex Bibtex: @article{CaltagironeEtAl2019, title={Lidar-Camera Co-Training for Semi- Supervised Road Detection}, author={Caltagirone, Luca and Lennart, Svensson and Wahde, Mattias and Sanfridson, Martin}, journal={arXiv preprint arXiv:1911.12597}, year={2019} }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 93.04 % 91.85 % 93.62 % 92.46 % 2.87 % 7.54 % UMM_ROAD 93.90 % 94.39 % 94.70 % 93.11 % 5.73 % 6.89 % UU_ROAD 91.15 % 90.16 % 89.68 % 92.68 % 3.48 % 7.32 % URBAN_ROAD 92.94 % 92.29 % 93.14 % 92.74 % 3.77 % 7.26 %
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
This table as LaTeX

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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