Python Stuff and Real-Time Video Object Tracking

The new version of dlib is out today. As promised, there is now a full Python API for using dlib's state-of-the-art object pose estimation and learning tools.  You can see examples of this API here and here.  Thank Patrick Snape, one of the main developers of the menpo project, for this addition.

Also, I've added an implementation of the winning algorithm from last year's Visual Object Tracking Challenge.  This was a method described in the paper:

Danelljan, Martin, et al. "Accurate scale estimation for robust visual tracking." Proceedings of the British Machine Vision Conference BMVC. 2014.

You can see some videos showing dlib's implementation of this new tracker in action on youtube:

All these videos were processed by exactly the same piece of software.  No hand tweaking or any funny business.  The only required input (other than the raw video) is a bounding box on the first frame and then the tracker automatically follows whatever is inside the box after that.  The whole thing runs at over 150fps on my desktop.  You can see an example program showing how to use it here, or just go download the new dlib instead :)

I've also finally posted the paper I've been writing on dlib's structural SVM based training algorithm, which is the algorithm behind the easy to use object detector.