I totally agree that deep learning will have a significant impact on video surveillance/analytics.
I am not an expert in deep learning but just for fun, last December I downloaded and installed Nvidia's "Digits" application
Not being an expert in Linux, it took me most of the day to install it and get it running... but once I did I created several folders on the hard drive, filled them with different categories of images: people, trucks etc. trained it for 2 hours or so, and then once trained, I downloaded additional images (not belonging to the original training set) and it was able to accurately categorize each image i.e. tell me if it was a person, truck etc.... I was able to do this without any programming skill required, or any real understanding of how deep learning works so I encourage others on the forum to give it a go.
The good thing is I do not think this basic application of deep learning is blocked by patents (I could be wrong though.).
From memory, the problem was that even though the images had been scaled down to about 200 by 200, it still seemed to take the best part of a second to analyse each image. So for an NVR with 60 camera streams at 5 fps, this might be too much. Keep in mind it was using an expensive power hungry, heat generating GPU to do the analysis, which makes things problematic for your run-of-the-mill NVR hardware. There are some cloud based GPU clusters available, and I suspect some of the cloud based analytics services are using these in combination with DL (just a guess).
So I am thinking that for simple tripwire detection and motion detetion, where no scene analysis is required, perhaps the traditional approach (as covered by the OV trip wire patent for example) may be easier to implement at present, as it doesn't require GPU style dedicated hardware and can be done on a traditional CPU. But with processors like the one referenced in your link above, that could be embedded in the camera directly, one would think that would change.
http://www.movidius.com/solutions/vision-processing-unit