## Method

ANN Contextual Blocks [CB]

Submitted on 31 Mar. 2015 14:25 by
Caio César Teodoro Mendes (University of São Paulo)

 Running time: 2 s Environment: 1 core @ 3.4 Ghz (Python) + GPU

 Method Description: Machine learning approach using ANN and contextual blocks. Parameters: Latex Bibtex: @ARTICLE{Mendes2015ARXIV, AUTHOR = {Caio César Teodoro Mendes and Vincent Frémont and Denis Fernando Wolf}, TITLE = {Vision-Based Road Detection using Contextual Blocks}, YEAR = {2015}, JOURNAL = {}, archivePrefix = "arXiv", eprint = {1509.01122}, primaryClass = "cs.CV", note = "arXiv" }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 88.89 % 82.17 % 87.26 % 90.58 % 6.03 % 9.42 % UMM_ROAD 90.55 % 85.40 % 92.75 % 88.45 % 7.60 % 11.55 % UU_ROAD 86.13 % 75.21 % 86.47 % 85.80 % 4.38 % 14.20 % URBAN_ROAD 88.97 % 79.69 % 89.50 % 88.44 % 5.71 % 11.56 %
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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