Video Analytics In A Mine - Best Option?

Hello!

Well, according to me actually there may be 3 reasons for this kind of requirement.

The first possible one is that the Customer simply "likes" and trusts that camera with that encoder, maybe timely "pushed"...;))

The second one, more technical, may be for example that if the camera should need to go very deep and far (several hundreds of meters for example) inside the mine, probably it's more expensive to use IP cables (not for that long ranges) and optical fibers than long analogue cables (still more efficient for longer ranges) and encoders out of it. But this if we have the camera inside the mine and the encoders at the control room outside. But if it is specified camera+encoder in a single "box" inside the mine, yes it looks quite a weird requirement actually: because anyway then you would need to carry IP signal for long range..

The third one, again technical, is if the requirement is for a radiometric information; thus, for a camera measuring the absolute temperature. For example, I can immagine that inside a mine it could be very interesting to see where there is a peak of heat to prevent digging in critical dangerous spots where, for example, a dangerous hot gas may explode or whatever else.. And to do this, it's definitely needed a radiometric camera, not a differential one like the one you mention: in that case you need to know "80°", not simply "there is something with a different temperature out there..".. Is the specified model of Flir camera a radiometric one or a differential one (which kind of microbolometer, in few words..)?

May it be one of these 3 reasons?..

In case of "none of the above ones", I fully agree with you it would be more convenient to use an Axis Q1931 camera with analytics onboard. Of course I can't confirm and suggest to you to use AgentVI, for obvious reasons.... (I'm from TechnoAware....;)))))....).. But of course that's not related with the problem...))))

Cheers,

Simone

(TechnoAware)

What about keeping it simple. Since thermal cameras are low resolution, use the analog output and connect straight to a Video IQ encoder located at the headend. It's much less expensive than a Rialto Blade. It could then interface into an ExacQ platform . Is there any need to monitor data from the Flir cameras?

The solution is simply to detect intruders on the perimeter. Really trying to get an idea what the best design would be for implementing and what the rest of the industry is doing.

I see it as a negative to take a thermal camera and convert it to analogue in the fieled and not have it on the network for remote configuration. Kind of seems counter productive to take a digital signal convert it to analogue and then encode it back to digital. Plus the added points of failure on the perimeter by haveing 2 hardware devices.

I can understand that this may be a viable solution if it is cheaper, but is it really cheaper? It seems that most of the video analytic "encoder" hardware only caters for analogue feeds and not IP. Also I get the impression that analytic encoders are the most popular way of implementing video analytics for intrusion detection.

Hi Simon.

So as I understand it you are saying do away with the anlytics encoder it is cheaper and better design to load an analytics app in the camera. Add benefit is less load on server and less bandwidth.

Secondly you are saying detction distance of thermal cameras need to be considered carefully in the worst case senario.

I find it interesting that you got 570m detection with a 60mm thermal, when the brochure says 1200m detction of a person, your example is half of what the spec sheet says. Further more you are saying that this specification is best case senario. So 300m detection would be more accurate (25% of stated detection range of manufacturer). Ontop of this you are saying the first 50m is actually a dead zone, so you actually end up with a usauble detction zone/area of under 250m with a 60mm thermal?

Is there a formulae to work out max detction range for thermal cameras when combined with video analytics? and what percentage should you subtract for moderately bad conditions such as rain when designing these perimeter systems? 30%

Hi Simone

I have now looked at your company website and see your recommendation in perfect conditions for detction is 100 pixels or 10 pixels per meter at the furthest point, as well as 10 consectuvive frames before detction at 8fps.

This is the formulae I was looking for.

Would be nice to have a reference chart for each popular model of thermal with minimum and maximum recommended detection ranges. Thanks

A lot of good (and curious) comments in this thread.

First off, IMO, the fundamental idea of "less is more" is certainly true here. Less devices in the field, less moving parts, less things to break and maintain is certainly the ideal approach. You have to weigh that against what is the overall cost of the various solutions. I think when you look at all the factors and the state of technology today, you'll find that there aren't very many acceptable options for edge analytics where the analytic comes from a 3rd party running on some other camera hardware.

Axis has some native on-board edge analytics, tripwire and coarse motion detection. This isn't a terrible choice for when you're looking for a simple solution covering a small area in a low activity outdoor environment. By low activity I mean minimal motion at all, not just low people activity. It's fairly cheap and serves a certain set of use cases OK. I don't think even Axis is going to tell you this is a head-to-head competitive option to more advanced products though.

You can also use some analytics software that is designed to be hosted on a general purpose camera. This option tends to be a little better, but the cost increases significantly, as does the setup and maintenance complexity. If you have full time operators that are highly trained in both systems they might be able to get by with this because they can continuously "tweak" stuff to slowly filter out false alarm objects over time. It can be high-maintenance, but workable.

Server-based systems are pretty rare these days, but there are a few that exist. They tend to fall between the "free" analytics, and the good edge-based stuff. Giving you decent performance in less demanding environments where the overall performance of the analytics isn't one of the top-3 measured criteria for the deployed system.

If you test all these options and look at total system cost, ongoing setup and maintenance costs, and false alarm rates you'll likely come to the conclusion that your best option is most likely to find a thermal camera optimally suited for the viewing task, and an analytics option optimally suited for the analyzing task.

There are lots of thermal camera options out there, one thing I always suggest people look at when evaluating cameras is the overall exposure control options on the camera. IMO, FLIR has some of the best knobs and dials in this capacity. Ideally, you can go with out-of-the-box settings, but like optical cameras, you may need to tune things for the specific environment to get the highest contrast image.

We (VideoIQ) can take an analog or IP feed from a thermal camera and add our edge storage and analytics with our Rialto A4 or I4 respectively. I think you'd find that relative to the other options discussed (and not discussed) so far, we'll end up giving you the best overall performance and flexibility in this scenario. Of course, I'm paid to say that, but I only cash those paychecks because I firmly believe in the product.

Simone also makes some good points in his comments, you need to factor your design according to what the customer really expects or is willing to tolerate. His numbers are a little more conservative than what I usually tell people, but it gets to the same point. You are essentially building a sensor network, if you try to push those sensors to the limit, then you are likely to suffer from decreased performance, just like any other sensor.

For thermal cameras we've been working pretty closely with FLIR over the years. I've come up with the following chart that shows typical semi-conservative coverage ranges/areas for various FLIR cameras coupled to our Rialto analytics appliance:

This is neither a "best case" or "worst case" chart, it's designed to show typical deployed coverage ranges. This would mean you have decent object contrast, clear shot (you're seeing most of the person, they're not heavily obscured by foliage for example) and so forth. It's designed to have enough pixels on target to analyze things properly to intelligently IGNORE as well as DETECT. Also, this isn't showing all the FLIR models, it was focused heavily on the newer FC cameras, as those have been tremendously popular lately, but you can use the same basic HFOV data for other F-Series cameras not listed here.

Obviously, as you try to opt for longer ranges you run greater risk of various things in the environment preventing you from getting good clear shots over the entire distance. I don't recommend you try to get detection beyond about 1000' unless you fully understand the environment and all of the components of the solution.

The next question people usually ask is "what rule works best". In most of our deployments it's pretty simple: you draw an ROI (Region of Interest), and tell the system to alert you when a person is active in that ROI during a specified time period. It can be 24/7, or only at certain times or on certain days. Many people initially think that some kind of tripwire rule is best, but in many cases tripwire is a mask for a deficient product. It's used to reduce false alarms because the system is only looking at activity in a very narrow area, and then just looks for blobs of pixels crossing a line in a particular direction. This reduces false alarms by ignoring most of the image, but if something prevents you from seeing the person just as they are crossing the tripwire, they are "home free" in the scene relatively quickly. We are able to analyze the entire FOV continuously and accurately, so we don't need to do tricks with the rules to filter out nuisance objects. Ideally, we see the person as they enter the ROI and trigger an alarm, essentially acting as a tripwire arond the edges of the ROI. But, if there is some rain that night, or the person has snuck in next to an overgrown bush, and we don't get to "see" them until they are 20' into the perimeter (and past the point where any tripwire would have been) we will still generate an alarm. By making the analytics smarter, we reduce the need to overthink the rules.

There are also "behavior" analytics options, but that has been more marketing gimmick than reality. Customers know what they want... "Tell me when a person is entering my property". We don't need to develop a pattern of behavior in the area, we know what we want to catch, where we want to catch it, and when we want to catch it. Also, if there is lots of intrusions, you run the risk of those behaviors becoming "normal" over time. Similarly if the customer wants to run regular intrusion tests of their own on the system you want to ensure those activities don't contribute negatively to the system's profile of the scene.

Sorry for the long reply, I wasn't intending to write that much, but hopefully it gives you some additional things to consider. As always, if you'd like to challenge my recommendations you're welcome to get an eval unit as test it for yourself :)

Thanks Brian for the insight. Your recommended ranges back up what Simone has said and is very usefull for design guidelines, still can't get over the difference opposed to the sales hype about the ranges. I stronly believe the thermal camera manaufactures need a standard for detction in their brochures that matches what is possible in real life scenarios as used by analytics.

Interested in your comment regarding monitoring a region of interest opposed to a tripwire, this certainly has set some alarm bells off with regards to some of the video analytics I have seen.

Additionally it appears both of you are indicating that not all thermals are the same and that certain thermals produce betetr contrast and lower noise than others and that this will also play a small role in the detction range.

Another important point seems to be that detction is one thing, but the number of false alarms a system produces is another.

The important value in false alarms rating is not only how many they may be, but how much do they cost, their verification and management.

For example, a very good barrier system may give 1 false alarm each 6 month (if installed in very stable ground!!.. try on herb or grass and you will maybe cry...)? Ok, let's even assume this for example. But if there is no videosurveillance and remote connection, how can the operator verify the alarm? It receives a "call" or a contact, but then he needs to stand up, maybe to make some kilometer and go to see. And searching where? Searching what? Maybe it was a dog, maybe 4 criminals who as he comes they hit him... So, 1 false alarm per 6 months; but a very expensive one.......

If you instead have a control room, with operators and a good VMS installed (you wrote they have Milestone, perfect) receiving an automatic real time alert from a good video analytic, maybe you can more than 1 false each 6 months, but their cost is to raise the head, to see a picture in a screen and to say "oh, just a dog.. farewell"... So maybe more false alarms (maybe..), but the total amount of the whole cost is much much much cheaper...

Then, of course the problem is when you don't use real video analysis, but just simple motion-detection-based products. In that case, even with thermal cameras, in outdoor you may have several falses per hour and of course even managing them by Milestone it's going to be too much!.. But if you use real video analysis, with thermal cameras you can reach with no problems performances such as much less than 1 false per month (I always mean per camera of course)..

Cheers,

Simone