Question: I would like to track humans' path with an (later with many) Asus Xtion looking down from the ceiling.

OpenNI's sample program called UserTracker (which uses the `User Generator` node) would be perfect, if it recognized bodies from top view, but it can't. Because I don't need exact skeleton tracking, just to track basically any moving objects on the screen, I guess there's an easier way for this than rewrite the User Generator's recognition, as I saw it would be tough.  

Maybe the `Scene Analyzer` node would be good for this, but I don't know how.

So anybody have an idea where should I start, which OpenNI classes should I use and how?  
I searched a lot about this and got into OpenNI as I can, but I'm kinda new to it and it's so deep.

Thanks, Ts.

Answer: 
**OpenNI:** For learning how to use OpenNI, take a look at [OpenNI Cookbook](https://www.packtpub.com/application-development/openni-cookbook), and if you don't want to buy the book, you still can use the source codes implemented in the book [here](https://www.packtpub.com/books/content/support/10884).

**OpenNI + OpenCV:** If you want to use OpenNi and OpenCV together, you can follow [this documentation](http://docs.opencv.org/doc/user_guide/ug_kinect.html). If you want a sample code, you can use [this source code](http://seevisionc.blogspot.com/2013/12/opencvkinect-acquiring-kinect-data.html).

**Labeling + Tracking:** For labeling and tracking the objects (in your case bodies), there are lots of implementations available online. As an example, for labeling, take a look at [this thread](https://stackoverflow.com/questions/12688524/connected-components-in-opencv). 
For tracking, you can use [this example](https://www.youtube.com/watch?v=X6rPdRZzgjg). 

For both labeling and tracking together, you can use [this one](https://code.google.com/p/cvblob/).
You can also take a look at [OpenPTrack](http://openptrack.org/about/) project.
OpenPTrack is an open source project launched in 2013 to create a scalable, multi-camera solution for person tracking.
It enables many people to be tracked over large areas in real time.
It is designed for applications in education, arts, and culture, as a start