Yann LeCun gave a webinar for NVidia a few weeks ago[1] which used a fairly high-powered laptop to do real time recognition of objects from a video feed. Overfeat[2] has also had this for a while.

There are some projects which can run pretrained deep nets in a few seconds on a smartphone, but they are definitely not "real-time" to my knowledge, though NVidia's upcoming unification of Tegra with their desktop line (and CUDA support!) may change this in a hurry.

sklearn-theano[3] doesn't support the "video stream" classification that Overfeat does, but we are planning to add it in the near future cause it is awesome :)

[1] http://on-demand-gtc.gputechconf.com/gtcnew/on-demand-gtc.ph...

[2] http://cilvr.nyu.edu/doku.php?id=code:start

[3] http://sklearn-theano.github.io/

I don't think the recognition is the challenge but rather the training of the model. Like imagine taking a really long time to compute a good linear regression equation, but then to use it you just plug in the values.

That said, this deep convolutional neural net for ImageNet with 60 million parameters and 650,000 neurons took 6 days to train on two GPUs [0]

[0] http://deeplearning.cs.cmu.edu/pdfs/1028/ImageNet.pdf