This is an implementation of the articulated pose estimation algorithm described in [1]. Much of the code is built on top of the implementation of mixtures-of-parts [2] and deformable part models [3].
To illustrate the use of the training code, this package also includes images from the Leeds Sports Pose (LSP) Dataset [4], and the negative images from the INRIAPerson dataset [5].
Prerequisites: The code requires Caffe with customized matlab wrapper (i.e. matcaffe.cpp) to run.
Acknowledgements: We graciously thank the authors of the previous code releases and image benchmarks for making them publically available.
[1] X. Chen, A. Yuille. Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations. NIPS'14
[2] Y. Yang, D. Ramanan. Articulated Pose Estimation using Flexible Mixtures of Parts. CVPR'11
[3] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. TPAMI'10. http://www.cs.berkeley.edu/~rbg/latent/index.html
[4] S. Johnson, M. Everingham. Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation. BMVC'10. http://www.comp.leeds.ac.uk/mat4saj/lsp.html
[5] N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection. CVPR'05.
- Install Caffe with the customized matlab wrapper under external/caffe. A symbolic link will work.
- Make sure to compile the Caffe MATLAB wrapper, which is not built by default: make matcaffe
- Start matlab, run 'startup' script.
- Run the 'compile' script to compile the mex functions. (the script is tested under ubuntu 12.04, you may need to edit it depending on your system)
- Run 'demo_lsp' to doing training and inference on the LSP dataset with benchmark evaluation.