dmikushin/libretracker

Libre, Free and Open-Source Eyetracking based on TIMM method

Libretracker: Libre, Free and Open-Source Eyetracking based on TIMM method

Introduction

Libretracker optimizes the Tristan Hume's original code of eye tracking algorithm by Fabian Timm (see references).

Building

Prerequisites for Fedora-like distros:

sudo dnf install git cmake opencv-devel ocl-icd-devel

Prerequisites for Debian-like distros:

sudo apt-get install git cmake libopencv-dev ocl-icd-opencl-dev

Special prerequisites:

• Needs at least GNU C/C++/Fortran 8.3.0 for AVX512 code generation

Older Ubuntu distributions might use PPAs to access newer compilers:

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo add-apt-repository ppa:ubuntu-toolchain-r/ppa
sudo apt-get update
sudo apt-get install gcc-8 g++-8 build-essential

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 80
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 50
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 80
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 50
sudo update-alternatives --install /usr/bin/gfortran gfortran /usr/bin/gfortran-8 80
sudo update-alternatives --install /usr/bin/gfortran gfortran /usr/bin/gfortran-5 50

Compiling:

git clone --recursive https://github.com/dmikushin/libretracker.git
cd libretracker
mkdir build
cd build
cmake ..
make -j4

Deployment

This version of libretracker reads the input image series from data/images/trial_0/raw directory, and saves outputs to data/images/trial_0/processed:

./libretracker

If a GPU is found, it is used by default. CPU version could be enforced explicitly:

USE_CUDA=0 USE_OPENCL=0 ./libretracker

The CPU version is single-threaded by default, unless the OMP_NUM_THREADS is given:

OMP_NUM_THREADS=4 USE_CUDA=0 USE_OPENCL=0 ./libretracker

Note libretracker's mulththreading backend is Intel TBB (not OpenMP), yet using the OMP_NUM_THREADS for unification.

Performance results

The results below are obtained on the provided test dataset. The time values are per frame, averaged from the entire dataset.

Intel(R) Core(TM) i7-3610QM CPU @ 2.30GHz:

TIMM version	time, ms
basic code	36.07
AVX vectorization, 1 thread	13.63
AVX vectorization, 4 thread	5.92
NVIDIA GTX1060 GPU	3.48

References

Timm and Barth. Accurate eye centre localisation by means of gradients. In Proceedings of the Int. Conference on Computer Theory and Applications (VISAPP), volume 1, pages 125-130, Algarve, Portugal, 2011. INSTICC.