Method

Histograms of the normalized inverse depth and line scanning[la] [HID-LS]


Submitted on 4 Jul. 2018 04:16 by
Shuo Gu (Nanjing University of Science and Technology)

Running time:0.25 s
Environment:1 cores @ 3.0 Ghz (C/C++)

Method Description:
Histograms of the normalized inverse depth and line
scanning
for urban road detection
Parameters:
0.2m, 2cm; 1m, 5cm
Latex Bibtex:
@inproceedings{GuZYK17,
author = {Shuo Gu and
Yigong Zhang and
Jian Yang and
Hui Kong},
title = {Lidar-based urban road detection by
histograms of normalized inverse
depths and line scanning},
booktitle = {{ECMR}},
pages = {1--6},
publisher = {{IEEE}},
year = {2017}
}
@article{GuZYK18,
author = {Shuo Gu and
Yigong Zhang and
Xia Yuan and
Jian Yang and
Tao Wu and
Hui Kong},
title = {Histograms of the Normalized
Inverse Depth and Line Scanning for Urban Road
Detection},
journal = {{IEEE} Trans. Intelligent
Transportation Systems}
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 93.10 % 86.38 % 91.89 % 94.33 % 3.79 % 5.67 %
UMM_ROAD 94.89 % 91.46 % 95.37 % 94.42 % 5.04 % 5.58 %
UU_ROAD 89.81 % 82.33 % 88.11 % 91.58 % 4.03 % 8.42 %
URBAN_ROAD 93.11 % 87.33 % 92.52 % 93.71 % 4.18 % 6.29 %
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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