The KITTI Vision Benchmark Suite

Method

StixelNet II [StixelNet II]

Submitted on 15 Oct. 2017 12:29 by
Oran Shayer (General Motors)

Running time:		1.2 s
Environment:		1 core @ 3.0 Ghz (Matlab + C/C++)

Method Description:

Parameters:

Latex Bibtex:

@InProceedings{Dan_Levi_2017_ICCV,
author = {Garnett, Noa and Silberstein, Shai and
Oron, Shaul and Fetaya, Ethan and Verner, Uri
and
Ayash, Ariel and Goldner, Vlad and
Cohen, Rafi and Horn, Kobi and Levi, Dan},
title = {Real-time category-based and general
obstacle detection for autonomous driving},
booktitle = {5th Workshop on Computer Vision for
Road Scene Understanding and Autonomous Driving
(CVRSUAD'17, IEEE-ICCV 2017
Workshop)},
month = {Oct},
year = {2017}
}

Evaluation in Bird's Eye View

Benchmark	MaxF	AP	PRE	REC	FPR	FNR
UM_ROAD	94.05 %	85.85 %	91.30 %	96.98 %	4.21 %	3.02 %
UMM_ROAD	96.22 %	91.24 %	95.13 %	97.33 %	5.48 %	2.67 %
UU_ROAD	93.40 %	85.01 %	91.05 %	95.87 %	3.07 %	4.13 %
URBAN_ROAD	94.88 %	87.75 %	92.97 %	96.87 %	4.04 %	3.13 %

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.

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