What Are The Most Important Problems That We Can Now Solve With Deep Learning? Part 1.

In previous discussion, I explained the reasons behind the incredible progress in video-analysis: Deep Learning hardware acceleration. Imagine now that we could have video-analytics working better than humans. What kind of features would be available to us? What would be the most important problems we would want to solve?

Some years ago, I had an interesting public discussion with John in one of the threads here. I had promoted forensic searching as the most important whilst John promoted real-time analytics. His argument was strong: real-time analytics can potentially prevent crime, forensic search - only investigate it. My argument came from a business perspective: real-time analytics would only serve systems that need to respond to situations. Meanwhile, investigative capabilities would be required for almost every system. Put another way, forensic search is necessary in every system; real-time analytics only in some.

So, let’s see what we can improve in both fields: real-time alerts and forensic search. Let’s start with forensic search. Real-time alerts will be the topic of the next discussion.

Forensic search

As far as forensic search was concerned, it was easy to sell unique features: face search, LPR search, color search, size, direction, dwell time, number of objects search and so on. This is what you never expect from typical VMS or NVR. So, this is an area where we can win. All great features to impress, but when it came to real-world installation, there was one big problem. To generate metadata to enable forensic search, we needed 50 times more resources on the server side. You can connect 1,000 cameras per server without analytics, but just 20 if you want to decompress and analyze all streams. So, not much business.

Hardware acceleration is the key

As I explained in previous discussion, the deep-learning booster was hardware acceleration. And not only on server side. Camera processors in every camera have become very powerful! One of the such processors HiSilicon chip 3516AV200 has two A7@800MHz cores plus one A17@1.25GHz core – so this chip can run powerful analytics!

What does this mean for forensic search adoption? Metadata can be generated on the camera side with zero additional CPU utilization on the server side. This means you can connect 1,000 cameras to one server – at no additional cost for the inclusion of these highly valuable features. Now, that is business!

All these years, we had no progress in user experience with archive search. Till now, just as 20 years ago, users could only choose camera number, data/time and playback. That was all. But because of hardware acceleration in video analysis on the camera side, users can now access many more tools to increase the efficiency of their work. Like TimeCompressor and MomentQuest from AxxonSoft.

To enable that features at zero CPU utilization on server side we at AxxonSoft have Bosch IVA analytics integrated, as well as Axis ACAP module and Dahua DHOP module for open platform cameras. We have also integrated HikVision’s metadata from thermal cameras, this approach will be implemented in the new 5 and 7 series, which will be released this year. What this ultimately means is that the solution is already there: a powerful forensic search at zero cost! So, finally, we have a dramatically improved user experience when it comes to searching archives. And in one of our next discussions, I’ll demonstrate how effective this technology is in different scenarios, from policing through to retail.

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