The system is implemented in MATLAB, with the most computationally expensive tasks written as mex functions able to exploit multi-core CPUs thanks to OpenMP support.
The implementation has been tested under Mac OSX and Ubuntu Linux. In order to build and use the system, the following are required:
- MATLAB (tested on R2011b and R2013a)
- gcc compiler (version 4.7 or greater)
- OpenNI 1.5 (not compatible with OpenNI 2)
Compile mex functions from an OS terminal with the following command: MATLABDIR="/path/to/matlab" OPENNIDIR="/path/to/openni/include" make.
Where "/path/to/matlab" is the MATLAB root directory, and where "/path/to/openni/include" is the OpenNI headers directory.
Up to this stage you can use the system:
- live: with an OpenNI compatible RGB-D sensor (tested with Microsft Kinect and Asus Xtion Pro Live), directly connected to computer where the system is running.
- recorded videos: with ".oni" files, previously recorded with an RGB-D sensor.
If you want to integrate the system in a ROS environment, reading sensor data on the topics "/camera/depth_registered/image_raw" and "/camera/rgb/image_raw/compressed", reading odometry data on the topic "/odom" and publishing the traked people on the topic "/people" with message type "people_msg/People", you need to perform the following additional steps:
- copy the entire people_msg directory into your
ROS_PACKAGE_PATH - cd to
ROS_PACKAGE_PATH - build the package with
rosmake people_msg
The files in the direcotry examples contain detailed explanations about the usage of the system with the various source type. To obtain more information about a particular function use the MATLAB command help.
Testing datasets, qualitative results and more details about the system are available at bit.ly/perceivingpeople.