Method

Hybrid Conditional Random Field [la] [HybridCRF]


Submitted on 21 Feb. 2017 08:04 by
Liang Xiao (National University of Defense Technology)

Running time:1.5 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Fuse Camera and LIDAR with Hybrid CRF model.
Parameters:
Latex Bibtex:
@article{XiaoHybridCRF,
author = {Xiao, Liang and Wang, Ruili and Dai,
Bin and Fang, Yuqiang and Liu, Daxue and Wu,
Tao},
journal = {Information Sciences.},
number = {XX},
pages = {1--11},
title = {{Hybrid Conditional Random Field based
Camera-LIDAR Fusion for Road Detection}},
year = {in press},
doi =
{https://doi.org/10.1016/j.ins.2017.04.048},
url =
{http://www.sciencedirect.com/science/article/pii
/S0020025517307119}
}

Evaluation in Bird's Eye View


Benchmark MaxF AP PRE REC FPR FNR
UM_ROAD 90.99 % 85.26 % 90.65 % 91.33 % 4.29 % 8.67 %
UMM_ROAD 91.95 % 86.44 % 94.01 % 89.98 % 6.30 % 10.02 %
UU_ROAD 88.53 % 80.79 % 86.41 % 90.76 % 4.65 % 9.24 %
URBAN_ROAD 90.81 % 86.01 % 91.05 % 90.57 % 4.90 % 9.43 %
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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