## Method

mixedCRF [la] [MixedCRF]

Submitted on 8 May. 2017 09:14 by
Xiaofeng Han (School of computer science and engineering, Nanjing University of Science and Technology, China)

 Running time: 6s Environment: 1 core @ 2.5 Ghz (Matlab + C/C++)

 Method Description: CRF based on liadr point clouds and images Parameters: Latex Bibtex: @article{Han2017Road, title={Road detection based on the fusion of Lidar and image data}, author={Han, Xiaofeng and Wang, Huan and Lu, Jianfeng and Zhao, Chunxia}, volume={14}, number={6}, pages={172988141773810}, year={2017}, }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 91.57 % 84.68 % 90.02 % 93.19 % 4.71 % 6.81 % UMM_ROAD 92.75 % 90.24 % 94.03 % 91.50 % 6.39 % 8.50 % UU_ROAD 85.69 % 75.12 % 80.17 % 92.02 % 7.42 % 7.98 % URBAN_ROAD 90.59 % 84.24 % 89.11 % 92.13 % 6.20 % 7.87 %
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.

## 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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