It's a very interesting concept.
There are a lot of machine-learning developments, and in Boston last year there was a job fair and conference focused on computer vision applications.
I think we may start to see more progress in security video analytics from the Google and Facebook projects that are tackling a slightly different problem, but in a way that can be leveraged in security.
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.
Thanks for the article. Until I read this I didn't think much about an announcement we did. Here is the product:
FLIR BOSON - Techcrunch: "FLIR and Movidius create the smartest thermal camera out there"
Greg from FLIR
It is really a good example of what I expect to see in a future. The breakthrough in the development of the artificial neural networks makes the computer vision area is much more "user friendly". You won't need a huge team of experienced pros to recognize something. Just take the ready to use artificial neural network and use it in your application. We will see the same situation as with the video surveillance market now.
I'm really curious to see where this all goes. 8 years ago, we were all in a tizzy about video analytics, and how the world would never be the same. A lot of new companies popped up, and a lot of investors threw money at them. At the end of the day, it really fizzled, and never delivered on the promises. I'm wondering if Machine Learning will be more of the same.