## Method

Hierarchal Inference Machine [HIM]

Submitted on 21 May. 2015 00:46 by

 Running time: 7 s Environment: >8 cores @ 2.5 Ghz (Python + C/C++)

 Method Description: A hierarchical Graphical Model for an image is build. For each layer in the hierarchy a Random forest is trained to classify. Parameters: Number of trees = 10 Max Depth of Trees = 10 layers in hierarchy = 7 Latex Bibtex: @inproceedings{Munoz2010ECCV, author = "Daniel Munoz and J. Andrew Bagnell and Martial Hebert", title = "Stacked Hierarchical Labeling", booktitle = "European Conference on Computer Vision (ECCV)", year = "2010" }

## Evaluation in Bird's Eye View

 Benchmark MaxF AP PRE REC FPR FNR UM_ROAD 90.07 % 79.98 % 90.79 % 89.35 % 4.13 % 10.65 % UMM_ROAD 93.55 % 90.38 % 94.18 % 92.92 % 6.31 % 7.08 % UU_ROAD 85.76 % 76.18 % 87.65 % 83.95 % 3.86 % 16.05 % URBAN_ROAD 90.64 % 81.42 % 91.62 % 89.68 % 4.52 % 10.32 %
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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