Code for anticipating human activities with rCRF
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build
data
pygraphcut-0.1
src
CMakeLists.txt
README.md
affmap.txt
classmap.txt
classmap_filtered.txt
install_dependencies.sh
pyglpk_setup.py

README.md

human_activity_anticipation

Code for anticipating human activities (http://pr.cs.cornell.edu/anticipation/)

If using this code please cite:

rCRF: Recursive Belief Estimation over CRFs in RGB-D Activity Videos Ozan Sener, Ashutosh Saxena Robotics Science and Systems (RSS) 2015

Anticipating Human Activities using Object Affordances for Reactive Robotic Response, Hema S Koppula, Ashutosh Saxena. Robotics: Science and Systems (RSS), 2013.

Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and Anticipation, Hema S Koppula, Ashutosh Saxena. International Conference on Machine Learning (ICML), 2013.

============================================

Dependencies:

  • OpenCV version 2.4 or greater (dev version or install from source)
  • PCL 1.6
  • Boost version 1.50 or greater

Commands to install the required dependencies and run anticipation code on Subject 1 data:

# install pcl
sudo add-apt-repository ppa:v-launchpad-jochen-sprickerhof-de/pcl 
sudo apt-get update
sudo apt-get install libpcl-all

# install opencv
sudo apt-get install libopencv-dev
#install boost 1.50
wget http://sourceforge.net/projects/boost/files/boost/1.50.0/boost_1_50_0.tar.bz2
tar --bzip2 -xf boost_1_50_0.tar.bz2
#If you prefer to install boost to a specific directory use the following instead
# ./bootstrap.sh --prefix=path/to/installation/prefix
./bootstrap.sh
./b2
sudo ./b2 install

# download code 
git clone https://github.com/hemakoppula/human_activity_anticipation.git

# compile
cd human_activity_anticipation/build
cmake ..
make
cd ../src/pyobjs
make

#install learning code dependencies
cd ../../
sh install_dependencies.sh

# download data
cd data/
wget http://web3.cs.cornell.edu/pr/CAD-120/data/Subject1_rgbd_rawtext.tar.gz
wget http://pr.cs.cornell.edu/humanactivities/data/Subject1_annotations.tar.gz
tar -xvzf Subject1_annotations.tar.gz
tar -xvzf Subject1_rgbd_rawtext.tar.gz
mv  Subject1_rgbd_rawtext/*/*rgbd.txt  .
mkdir objects
mkdir objects_tracked
cp Subject1_annotations/*/objects/* objects/
cp Subject1_annotations/*/objects_tracked/* objects_tracked/
cp Subject1_annotations/*/*.bag .
cp Subject1_annotations/*/*.txt .
cat Subject1_annotations/*/activityLabel.txt  | grep -v END > activityLabel.txt
cat Subject1_annotations/*/labeling.txt > labeling.txt


# run anticipation code 
cd ../build/
./predict_seg ../data/ activityLabel.txt 1