The KITTI Vision Benchmark Suite

Method

Convolutional Neural Network [CN24]

Submitted on 30 Oct. 2014 15:32 by
Clemens-Alexander Brust (FSU Jena)

Running time:		20 s
Environment:		>8 cores @ 2.5 Ghz (C/C++)

Method Description:

3-layer CNN

Parameters:

Configuration:
CNet-N

Latex Bibtex:

@InProceedings{Brust2015:CPN,
Title = {Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding},
Author = {Clemens-Alexander Brust and Sven Sickert and Marcel Simon and Erik Rodner and Joachim Denzler},
Booktitle = {{VISAPP} 2015 - Proceedings of the 10th International Conference on
Computer Vision Theory and Applications, Berlin, Germany,
11-14 March, 2015},
Year = {2015}
}

Evaluation in Bird's Eye View

Benchmark	MaxF	AP	PRE	REC	FPR	FNR
UM_ROAD	76.28 %	79.29 %	72.44 %	80.55 %	13.96 %	19.45 %

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

Road/Lane Detection

The following plots show precision/recall curves for the bird's eye view evaluation.

This figure as: png eps pdf

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.

This figure as: png