Information

This package contains a matlab implementation of Pose-based CNN (P-CNN) algorithm described in [1]. It includes pre-trained CNN appearance vgg-f model [2], a matlab version of the flow model of [3] and the optical flow implementation of [4]. CNN implementation uses the MatConvNet library [5]. The project webpage is http://www.di.ens.fr/willow/research/p-cnn/ .

####To run this package:

• Prepare/download CNN models and data examples by running init.sh file from the P-CNN folder.
• This package compiles MatConvNet [5] in "CPU mode". To speed up computation you may want to enable GPU support (much faster). To help you, we provide the my_build.m file in the matconvnet-1.0-beta11 folder that you can modify.
• You may want to recompile Brox optical flow 2004 [4] (download sources).

####demo.m An example of P-CNN computation is given in this package. It computes P-CNN for few videos of the JHMDB dataset [6] (for 2 different splits) using pose ground truth annotations. The reproduce_ICCV15_results command reproduces the P-CNN results reported in [1]. Because we wanted to provide a "full matlab code", we converted all the code to matlab resulting to a slightly different result (-0.9% accuracy) from the published version due to the switch of the CNN package and retraining.

The provided algorithm takes as input the frames of a video and their corresponding pose joints (from ground truth annotation or from your favorite pose detector). There is a demo.m file in the package that you should be able to run.

####Cite If you use this package, please cite:

@inproceedings{cheronICCV15,
TITLE = {{P-CNN: Pose-based CNN Features for Action Recognition}},
AUTHOR = {Ch{'e}ron, Guilhem and Laptev, Ivan and Schmid, Cordelia},
BOOKTITLE = {ICCV},
YEAR = {2015},
}

####References [1] G. Chéron, I. Laptev, C. Schmid. P-CNN: Pose-based CNN Features for Action Recognition. ICCV 2015.

[2] K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. BMVC 2014.

[3] G. Gkioxari and J. Malik. Finding action tubes. CVPR 2015. ACM 2015.

[4] T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004.

[5] A. Vedaldi and K. Lenc. MatConvNet - Convolutional Neural Networks for MATLAB.

[6] H. Jhuang, J. Gall, S. Zuffi, C. Schmid, and M. J. Black. Towards understanding action recognition. ICCV 2013.

####Acknowledgements We graciously thank the authors of the previous code releases and video benchmark for making them publicly available.