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fast.fhog's Introduction

fast.fhog

Implementation for: Polceanu M., Harrouet F., Buche C., "Fast Multi-Scale fHOG Feature Extraction Using Histogram Downsampling", RoboCup International Symposium, 2018.


Information

This code uses a modified version of Dlib 19.4 (http://dlib.net/) which is included in this repository.

The training and testing datasets are included. Images are obtained from multiple sources, including the SPQR dataset (http://www.dis.uniroma1.it/~labrococo/?q=node/459).

Level up to which scheme is applied is hard-coded to 7 in dlib-19.4-modified/dlib/image_processing/scan_fhog_pyramid.h line:928 (link to line) (sorry, research code...). After changes, the library must be recompiled.

Other parameters are found in the same file as above, at lines 685-693.

Configurations:

Original baseline:

  • set USE_HACK to 0

Power law (Dollar et al. 2014):

  • set USE_HACK to 1
  • set hybridLevel to 10

Paper results:

  • set USE_HACK to 1
  • set hybridLevel to values between 2 and 9

Note: Average precision on the testing dataset will be lower than those reported in the paper. Results from the paper were obtained by doing a hyperparameter search at each level. This implementation has hardcoded hyperparameters and is only optimized for the original baseline.


Instructions

git clone https://github.com/polceanum/fast.fhog.git
cd fhog
cd dlib-19.4-modified
mkdir build
cd build
cmake ..
make -j
cd ../..
mkdir build
cd build
cmake ..
make -j
./learnRobot ../dataset ../dataset

Sample output

[...]

objective:     1398.83
objective gap: 1398.82
risk:          46.8166
risk gap:      46.8166
num planes:    3
iter:          1

objective:     506.993
objective gap: 506.687
risk:          16.9581
risk gap:      16.9581
num planes:    4
iter:          2

objective:     772.134
objective gap: 771.459
risk:          25.8197
risk gap:      25.8197
num planes:    5
iter:          3

[...]

hack time: 0.00600257
avg hack time: 0.00665809
correct_hits=161 total_hits=168 total_true_targets=185
testing results (precision, recall, average precision):  0.958333  0.87027 0.865549

[...]

Robot detection:

alt text

Learned vignette:

alt text

Extreme lighting conditions:

alt text

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