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homography tracking
License: GNU General Public License v2.0
This project forked from albertocrive/homographytrackingdemo
homography tracking
License: GNU General Public License v2.0
This code has been presented for the demo : "Tracking texture-less, shiny objects with descriptor fields." Mixed and Augmented Reality (ISMAR), 2014 IEEE International Symposium on. IEEE, 2014. It allows to track homographies using the descriptor fields introduced in our CVPR paper: [1] Crivellaro Alberto and Vincent Lepetit. "Robust 3D Tracking with Descriptor Fields.", CVPR 2014 Please cite [1] if you make use of this code. Authors: Alberto Crivellaro Yannick Verdie Kwang Moo Yi Pascal Fua Vincent Lepetit For questions or comments please write to : alberto<dot>crivellaro<at>epfl<dot>ch ================================================================================ BUILDING: The software has been tested and successfully run in real time (> 30 fps) on linux Ubuntu 14.04 on a MacBookPro i7 2.30GHz. It was built with gcc-4.9 and openmp 4.0. We also tested on MacOS with clang (no openmp) and we also obtained good performance (~20fps). Older versions of gcc/openmp could lead to worse performances. Prior to build the code you should install the (latest version of the) following dependencies: - opencv (we tested with 2.4.9 and 3.0) - eigen 3 or more Moreover, you should correctly configure your LD_LIBRARY_PATH and your PKG_CONFIG_PATH. For building just 'make allβ. The build produces : - libC++/libHomographyEstimation.so : a C++ dynamic library for computing descriptor fields out of a grayscale image, for computing the homography between 2 images and several utilities for display, I/O, etc. - ISMAR_Demo/homographyDemo : an executable for performing real time 3D tracking of planar surfaces., it links to the c++ library and can be use as example to understand how to link to the library. - unit/unittests : some tests ================================================================================ EXECUTING THE DEMO: ./homographyDemo <camera_device_number> you should initialize the template putting a planar surface in front of the camera (covering at least all the region in the black rectangle on the image), and then press: 1 for tracking using image intensities 2 for tracking using gradient magnitude 3 for tracking using descriptor fields The optimization parameters are in ISMAR_Demo/parameter/parameters*****Optimization.yml files. You can change them for attempting to achieve a better stability. ================================================================================ USING THE LIBRARY FOR YOUR PROJECTS: include libC++/HomographyEstimation.hpp in your code, and link with libHomographyEstimation.so . For examples on how to use the different utilities, you can take a look at the unit tests, more in particular : void LucaskanadeSSDTest(); void LucaskanadeDescriptorFieldsTest(); void LucaskanadeVideoSSDSpeedTest(); void LucaskanadeVideoDesciptorFieldsSpeedTest(); void LucaskanadeVideoGradientMagnitudeSpeedTest(); ================================================================================ USEFUL LINKS: homepage of the project: http://cvlab.epfl.ch/page-107683-en.html demonstration video: https://www.youtube.com/watch?v=r-WJDM6HyAs
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