The WYSIWYD project will create a new transparency in human robot interaction (HRI) by allowing robots to both understand their own actions and those of humans, and to interpret and communicate these in human compatible intentional terms expressed as a language-like communication channel we call WYSIWYD Robotese (WR).

Visit the official Project wiki for the software.

• Linux:
• Windows:

To enable the new feature you have to go through the following steps.

We need the new tracking features delivered with OpenCV-3.0.0:

1. Download OpenCV-3.0.0: git clone https://github.com/Itseez/opencv.git.
2. Checkout the correct branch: git checkout 3.0.0.
3. Download the external modules: git clone https://github.com/Itseez/opencv_contrib.git.
4. Checkout the correct branch: git checkout 3.0.0.
5. Configure OpenCV by filling in the cmake var OPENCV_EXTRA_MODULES_PATH with the path pointing to opencv_contrib/modules and then toggling on the var BUILD_opencv_tracking.
6. Compile OpenCV.

Configure the project:

1. Fill in the cmake var OpenCV_DIR with the path to OpenCV-3.0.0 build.
2. Toggle on the cmake option WYSIWYD_IOL2OPC_WITH_TRACKING. Note that if this options is OFF, then we do not need OpenCV-3.0.0 and the project still compiles with the previous versions.
3. Compile wysiwyd.

It might be the case that you have also to build kinect-wrapper with the new OpenCV library, as it was for me too. In particular, the setting I used foresees yarp, icub-main and all the other projects built with OpenCV-2.x.y, whereas wysiwyd along with kinect-wrapper built with OpenCV-3.0.0. This means that you can be perfectly working with two versions of the library available side by side. To ease compilation, I have also enabled the possibility to build only the client part of the kinect-wrapper (see updated instructions).

From an high-level perspective, the behavior is very simple: once the segmentation pipeline stops providing blobs due to big occlusions in the scene, then a tracker takes over it and keeps updating the object localization for a tune-able time window.

To regulate the temporal width of such a window, play with the tracker_timeout parameter of the iol2opc module, which is by default set to 5.0 s.