Mystique can be a great advantage in lots of professions. For example, movie stars seem more exotic and interesting the less we know about them. The attraction of traveling to distant countries and strange cultures is also boosted by the desire to see something obscure.

But I’m not sure mystique is a good thing when it comes to security systems, and that’s one of the problems I see with BRS Labs. They have generated market interest by their amazing claims, but there is very little known about their technology. That may be their biggest advantage – at least for creating marketing buzz – but it raises quite a few concerns when it comes to actually using their systems for security.

Let’s take their central claim that they can detect out of the ordinary behaviors without any rules being set up or configured. It automatically observes the area, learning what is typical. Then it alerts you when something happens that is abnormal.

It’s a truly fascinating marketing concept. Who wouldn’t want a system that you could just plug in and was smart enough to know when a potential threat was occurring – even warning you about things you would not have thought about before. At first it seems like the perfect answer to security.

However, the more I think about it, the more concerned I get. The problem is that we don’t know what it is going to detect or why, and we don’t know what it might miss that could be important. In other words, it is mysterious about how it works.

How do you know how effective the system will be unless you know what it is detecting and how it works? How do you know it is going to catch real threats that matter to you, if you don’t know its enigmatic methods of detection?

Being curious, I decided to do some research. I tracked down the first patents that BRS Labs filed to get a better idea of what was under the hood. And just like in the Wizard of Oz, once I pulled back the curtains and understood what they were doing, it lost a lot of its mystique.

The most important thing I learned: Their system isn’t smart enough to work without rules. Their system requires rules just like all analytics systems. The big difference is that they simply aren’t telling you what those rules are that the system is using. It can’t detect anything out of the ordinary, it can only detect the types of things they program it to look for. How can you ever judge how well the system is going to work unless they tell you what those rules are?

From what folks at BRS have said, their system watches where objects enter the field of view and where they go in the scene, including their direction of travel. I believe they also detect where objects stop and about how fast they move. They probably distinguish people from vehicles and seem to be able to filter out ordinary background movement. This is actually all stuff we do as well, as do many other analytics systems.

What is different, is that they monitor these specific activities over time, and if some pattern of actions happens that is different from previous activities, they consider that a potential threat. In other words, if a car parks in an area where the system hasn’t seen a parked car before, it generates an alert. It will do this whether you care about that or not. If you get the alarm and don’t care about it, then you can tell the system to stop sending alarms like that.

But here’s the problem: when you are telling it you don’t care about that kind of alarm, you can never be sure what you are saying you don’t care about. You might think you are telling the system that you don’t care about someone parking in that spot, but in fact it might have alarmed because it was a truck and it had never seen a truck in the scene before, or the truck might have taken a different path than usual. You are telling the system to stop sending those alarms, but you don’t even know what it is you are turning off – because you don’t know the rules it was using in the first place. So, you might be making the system worse.

You might think, then, that you should not tell it to stop sending alarms, but the system needs you to, because when the system starts up, it generates large quantities of alarms, because lots of things look abnormal at first. The false alarm rate would never be manageable if you didn’t teach it what was not important. This process takes weeks of training, from what I’ve heard.

If you were trying to protect a high risk facility, such as a nuclear power plant or a place were dangerous chemicals were stored, how secure would you feel if you never knew what your system was detecting and what it was ignoring? And how secure you would feel if you told the system something was unimportant, but you didn’t know exactly what you were turning off?

If I want to detect someone in an area that is off-limits at night, with a rules based system you define the detection you want, and you can easily measure if it is missing real threats or sending you false alarms. But how do you measure or judge the accuracy of a system when you have no idea what it is detecting or avoiding? I don’t think this is a place for mystique. I think this is a place where we need to know what the system is doing. Otherwise, how can we ever know if it was going to provide the kind of protection we need?

That’s the first big problem that concerns me about this idea. However, as I thought about it, another problem became clear as well. This is something that is important in security: How easily could someone defeat your system? In this case, it becomes clear that if you do something repeatedly, the system is going to start ignoring it, because it is no longer abnormal. So, if someone wants to defeat the system, they just need to do something over and over again, and they can be sure the system will stop alerting on that behavior.

For example, you might want to be warned whenever a car enters a parking lot at night. Well, if a smart terrorist or criminal knew you had one of those mysterious behavioral detection systems, they would simply make a habit of driving into the parking lot and turning around and driving out. The first few times it would generate an alarm and anyone looking at the video would probably think a person just came in by mistake and left. But even if you wanted to keep an eye on such behaviors because it could potentially be a problem, you would not be able to, because the system would start ignoring it once it happened often enough.

Hopefully BRS has a way for the user to tell the system that although an alert was not important, that it still wants to keep seeing them – and not to start ignoring those kinds of things. But the problem is that you don’t know exactly why it generated the alarm, and so you don’t know what it is you are asking for more of, or saying you don’t want to see any more.

The BRS Labs  systems would be a lot more useful if they told everyone exactly what their rules are for detection. However, this would probably rob them of the great mystique their system has, which has certainly created a lot of good marketing for them.

Mystery is a great attractor. But when it comes to security, I think we need to know how a product works before designing it into a system. Spice is nice, but it doesn’t make a good main course.

It is not easy finding public information about how video analytics products actually compare. We have been through side by side tests with quite a few companies, but they have all turned down our requests to publish the results for a variety of reasons. That’s one of the challenges of the security industry. There are often good reasons not to make what you are using public.

We were happy to see John Honovich step up to provide some open testing. As soon as he asked if we were interested, we sent him our iCVR. He ran a test and published it last year:

http://ipvideomarket.info/report/testing_videoiq_video_analytics_icvr

Unfortunately, from what we’ve heard, no other video analytics companies have stepped up and agreed to John’s requests. It’s hard to compare a technology when there is only one.

We were invited to join in on a side-by-side comparison at ISC East last October. Three video analytics companies were invited and agreed to run a test: IOimage, BRS Labs and ourselves (VideoIQ).

It was sponsored by Government Security News and about 100 people attended.

At the last minute, BRS Labs cancelled. I never heard why. So, it ended up only being IOimage and ourselves.

I will try to be objective about the results, but of course that’s not as easy as it sounds.

The demos weren’t long. We each had about 10 minutes to show our products working. Not exactly an in-depth test. Both systems had to detect someone walking from behind the curtains across the room. With our system they also tested “Object Missing” and with IOimage they tested “Object Left Behind”.

Both products worked fairly well. Some of the differences were minor but interesting.

IOimage needed to calibrate their system before the event. They used two people and it took them about 30 minutes. However, since it was indoors, they could cut their process short. In general it followed the videos that they show on how to calibrate their products, but these guys were clearly experienced and didn’t have much time, so they moved fast. One person sat at the PC and marked the head and feet of the other person at four different places in the room. They drew a line on the floor, showing a predetermined distance (10′ in this case). They only calibrated one axis, not two. And they only calibrated in the one area that was going to be tested, not the whole room.

Our system doesn’t need calibration, as I’ve mentioned before. So, we had plenty of time. Picture the Maytag repair guy with his feet up, while waiting. <G>

However, what we found interesting was the way IOimage positioned the whole process of calibration and tuning. They claimed calibration is what makes detection better. This is why they could detect someone crawling. However, as I’ve said before, this is wrong. While calibration clearly makes their system work better, in our case the calibration runs automatically. So, it is really a question of manual calibration compared to automatic calibration. As I’ve pointed out, there are lots of big disadvantages to manual calibration:

http://spotonsecurity.com/2009/11/05/the-tuning-and-calibration-controversy/

The issue about detecting crawling people is bogus. Perhaps they don’t realize it. They might think that calibration has something to do with this, but it doesn’t. It is simply that we have not yet developed an object classification type for people crawling. Quite frankly, we haven’t had any demand for it yet. The fact that we can still detect people crawling is easily demonstrated by setting our detection to “suspicious objects”, which easily captures people who crawl.

On the other hand, IOimage worked hard to make sure that they were getting a full view of the people in the area they wanted to detect, and they were especially concerned about moving chairs or tables out of the way. We didn’t have to worry about that, because we have an object classification type for head-and-shoulders detection of people. That’s pretty important indoors, because chairs and desks are quite common. IOimage apparently doesn’t have that object classification type, which is why they needed to see the whole person. In other words, this has nothing to do with calibration; it is all about the types of objects the system has been trained to detect.

You can read my previous post about the down side of manual calibration. But here are some other questions: How do you calibrate boats? Does someone have to walk out on the water? If  you are setting up a system to watch the tarmac at an airport, do you have to shut the runway down while you walk out there to calibrate it? What about hazmat sites? Do you really need to send people in there to calibrate the camera? Besides all the other problems with having to manually calibrate a system, as I mentioned in my previous post, it isn’t always practical.

The real test comes down to how well do the systems work. That’s what really matters. Unfortunately, this wasn’t a stringent test. We wished it could have been outdoors with trees blowing in the background, to make it a tougher comparison. In general, both systems seemed to work well. We spotted some minor false detects on their system when the curtains moved, high up in the air where calibration should have ruled detection out, but for some reason didn’t.

Another noticeable difference was that they were only streaming the video from their camera, while our camera captures and records the event. Therefore, it appears to be more difficult with their system if you missed what just happened. In our system, you get a video clip that you can playback whenever you are ready. You don’t have to be there watching. IOimage would have to add an NVR to their system to get storage playback.

What IOimage added instead, that we don’t have, is mouse trails of where a person has been in the scene. This helps when it isn’t as easy to see where someone has been. You can just look at their previous trails. That works. I would rather watch the actual pre-alarm video so you can see what they were actually doing, but both systems offer something that works.

Apparently, from what we could see, IOimage can adjust sensitivity, but it seems to change it for the whole camera. We can individually set sensitivity for each detection rule. So, our missing object can be very sensitive, if we want, but people detection does not need to be.

One of the best things about their product is their web interface. Very well done. An integrator can do all of their calibration through the web, and it worked smoothly. If you have to calibrate, that’s a big plus.

However, they had to switch their camera from people detection to object left behind during the demo. Apparently it doesn’t detect both at the same time. Ours does, and in fact can run quite a few different types of detections with different types of objects, in different regions of interest, all at the same time on the same camera.

Also, once our rules are set up, our system keeps detecting even when you are changing or adding new rules. We were surprised to see that the IOimage system shuts down when you are setting up rules. It’s not a big issue. It is more important to detect a number of different things at the same time. That’s very useful.

It wasn’t much of a detailed competitive test, as I said. Both systems use high quality video analytics, not advanced video motion detection. And the results from both were pretty good.

And of course, this would be more objective if someone else were reporting it. However, it is so hard to find any comparison testing that I thought I would share it, anyway.

The CEO of BRS Labs, Ray Davis, has apparently refused to send their equipment to John Honovich for testing:

http://ipvideomarket.info/report/brs_labs_layoffs_and_skepticism_mount

I hope we aren’t the only ones who are pushing for more open comparisons. It helps everyone to see how technologies stack up.

I encourage our competitors to step up and send their products to John. Or let’s find some other approach to public testing.

When we first introduced our iCVR, I heard from a number of other video analytics companies. They offered their compliments. Many of them said that when a new market is gaining recognition, all successful products help bring growth to the whole field, so it was good for everyone. I was glad to hear them say that, because I feel the same way. It is nice to work in a field where there is open communication and friendly compliments, even though we are competitors.

But the one area that they thought we’d gone too far with was our marketing message that our product needed no tuning or calibration.

I said, “Well, it’s true.”

They looked at me  in a way that it made it clear they didn’t believe it was even remotely possible. They were thinking this was going to be another case of a company over-exaggerating their claims. That’s one of the worst things for new technologies, since it hurts credibility and people start doubting any of it works.

I reassured them that our claims were true, no matter how hard it was to believe. In fact, I said, even if someone wanted to, there is no way for an installer to tune or calibrate our iCVRs. That’s when they realized we really meant what we said.

For some people, our claims sound too good to be true. In fact, only a few months ago, Itsik Kattan, CEO of Agent-VI, said in an article: “Beware of ‘low-touch’ and ‘no-touch’ systems and vendor claims that their analytics self-learn. There’s no such thing.”

http://www.experteditorial.net/securitysquared/2009/08/video-analytics-business-intelligence-security.html?p=5

Lately, the language we’ve been hearing has created even more confusion. Some sales people are saying that calibration and tuning are required to get high accuracy, and any system that doesn’t use calibration and tuning are on the order of traditional video motion detection.

We’ve also heard from some who promote the idea that the more tuning knobs the better, and some products have more than 50 different adjustments that can be made. They feel they have the best accuracy as a result.

There are some serious flaws with these comments. However, the real problem here is that this kind of talk focuses on the wrong thing.

It isn’t about whether tuning and calibration can help make a system more accurate. We agree that it does. It’s hard to argue with that. The real question is which is better: Automatic self-tuning and calibrating systems, or manual systems?

We’ve got plenty of tuning going on in our iCVR. The moment it starts up it learns the environment, adjusts the filters is it using, and continues to get smarter over time. As the environment changes, it re-tunes and continues to adapt.

The calibration process also happens automatically.  The iCVR watches for people and vehicles and boats, and when it sees them it automatically calibrates what the proper height for these objects should be. This does indeed reduce false alarms, although it doesn’t help our systems as much as it helps other technologies.

The reason we can do the calibration automatically is because our accuracy before calibration is far better than any other product. If accuracy is poor to begin with, then you have to use manual calibration, because the system doesn’t know what objects are people and which objects are false alarms, like tree branches or birds or bushes blowing in the wind.

In fact, the worse a technology is to begin with, the more important calibration and tuning is.  Take advanced video motion detection (AVMD) systems that don’t even know what a human looks like: They are far more dependent on calibration and tuning than true video analytics systems. Why? Because AVMD doesn’t have any way of knowing it is a person except by the size of the object. Manual calibration is the only way of teaching the system what a human should look like.

Of course, AVMD even with calibration and tuning doesn’t even come close to true video analytics systems, as I’ve written about earlier on this blog.

But let’s get to the big issues that make this issue so important. Here are the advantages of a self-tuning and self-calibrating system:

  1. It saves hours of set-up time per camera. We’ve heard the average is between 3-8 hours per camera. That’s significant.
  2. You don’t need specially trained technicians to install it. Anyone who knows how to install a camera can do it. The more tuning knobs a system has, the more important it is to train every installer to get the best results. No tuning knobs means that anyone can install it.
  3. You don’t need to re-tune or re-calibrate every time a camera is moved. The system adapts automatically (note, if you set up detection for a specific region of interest, you may need to change the region of interest, which is why our system automatically tells you if a camera has been moved). For manual systems, if you don’t re-tune and re-calibrate, you are assured that the system is set up wrong after the camera is moved.
  4. If the environment changes, the system automatically re-tunes itself. When the leaves fall off the trees or when snow appears on the ground, you can be assured that re-tuning is needed to improve performance. But with manual systems, unless you manually re-tune it when the seasons change, you will have degraded accuracy and more false alarms.
  5. Self-tuning and self-calibrating systems are always at their best. Manual systems are at their best the minute after you tune it and calibrate it. After that, performance deteriorates. With our iCVR, performance actually gets better over time.
  6. Lastly, we are continuing to improve the performance of our analytics. Every time we do, we provide a downloadable update that can be sent to all the iCVRs in the field, thus improving their accuracy. But with a manual system, how do you send new algorithms and use the old tuning settings? With new algorithms, you need new tuning adjustments. So, it is a much more time consuming process to update to the latest improvements.

When you add all of these issues up, I don’t know how you argue that manual adjustments are better.

But the whole thing still comes down to how accurate the system is. Having an automatic technology that has poor detection and high false alarms is not going to be an improvement.

So, where does our iCVR stand?

When compared against the best of the best video analytics technologies, even if you bring in an engineer from their factory to do the tuning and calibration so you know it is absolutely adjusted the best it can be, from what we’ve seen and heard, we are about on par and maybe slightly better. In some cases we’ve seen that we beat them, but we’ve seen a few cases were they edge us out by a hair.

But that is the moment after tuning and calibration. Our system then continues to get smarter and more accurate, while all the manual tuning and calibration systems get worse. So, over time, we have the clear advantage in terms of accuracy.

We’ve had plenty of people test our stuff and have confirmed this, but it is always best to try this out for yourself and come to your own conclusion.

However, I think the rest of the benefits make the point pretty clear. The cost to install and maintain, and the ease of use are all in favor of systems that are smart enough to calibrate and tune themselves.

In other words, there really isn’t any controversy here.

It continues to amaze me that people list Baggage-Left-Behind detection as one of the shining examples of the power of video analytics. In fact, it has to be the biggest disappointment in the market.

However, the reason it has failed so often is not a reflection on bad technology, but the dangers of complex systems. It is a good lesson in knowing where and when to use new technologies.

First, it is important to say that there are hundreds of integrators now using video analytics with very positive results. They are going after the sweet spot, where analytics produce great returns on investment and solve real problems that would be difficult to solve any other way: detection of people where they aren’t supposed to be, perimeter protection, security for outdoor assets, remote guarding, storage and bandwidth savings, etc.

But we have heard so many stories of failed projects as a result of trying to make baggage-left-behind work that we have intentionally left it out of our product, and we regularly warn integrators of the dangers. I thought it would be worth sharing a little more information about it, to help others from falling into this trap.

If you want to see cases of proven failures, you can check out the lawsuit between Lockheed Martin and NYC:

http://www.nytimes.com/2009/04/29/nyregion/29mta.html?_r=1

Their problem wasn’t just about baggage-left-behind, but that was one of the applications that they simply couldn’t get to work. This doesn’t mean the technology couldn’t detect bags left behind. It means that even though it could, the system was unusable.

IBM made a study of baggage-left-behind years ago. I don’t see a public posting of the study that I can link to, but they went back over the last decade to study terrorist bombings. The question they asked was how much benefit would baggage-left-behind have delivered if it were installed at the sites that were bombed.

Their conclusion: Baggage-left-behind would not have helped a single one of the cases. No benefit at all. It wouldn’t have helped even prevent a single bombing or saved any lives.

We also had a recent discussion with someone from the I-LIDS group in the UK, which is a UK Home Office sponsored testing group for video analytics. They set benchmarks for a variety of video analytics requirements. Many have been effectively deployed. However, when it comes to baggage-left-behind, they told us that no vendor has come even close to meeting the needs of supplying the functionality needed to make the technology work as it would need to work to be successful.

The problem isn’t detecting a bag being set down or left behind. That’s the easy part. It is the complexity of the whole system and what is needed to make it practical.

Think about how often you might get an alarm if you detected any bag put down in a crowded airport. How often do people let go of their bags for a moment? How often do they walk away from their bags to throw a newspaper away in the trashcan, or leave their bags with a family member while going to the restroom? How often do people leave a newspaper behind or a lunch bag?

So, the first problem is that the system produces a ton of false alarms. Who wants to continue responding when false alarms are continuous and far outnumber real warnings?

But this is only one part of the problem.

The second part of the issue comes down to how often is a terrorist bomber going to leave their bomb out in the open in the middle of the floor? Even if they were crazy enough to do this, how long would terrorists continue leaving bombs out in the open once they knew baggage-left-behind was deployed everywhere?

Not many. Especially when it is so easy to put a package into a trashcan, or hide it from open view.

It is true that there have been cases of people leaving bombs behind under a seat or in a corner, but the moment word gets around that someone was caught by baggage-left-behind, the technology would be useless, as terrorists would soon stop leaving bombs in any visible place.

On top of these issues, you have the problem of accurately detecting objects that are left behind when you have a crowded area. When people are continuously walking in front of a bag, how long will it take before you detect it?

If the false alarm rate were really low, I’m sure some transportation facilities would still like to use it, even if the ability to detect a bag was low. However, when you add in the low, low likelihood of it actually catching anyone who is intentionally leaving a bomb, and the lower likelihood of it catching terrorists in the future, then the whole program simply becomes impractical.

This doesn’t mean it can’t be used some places effectively. I’ve heard of a couple. They were not crowded areas, and they were not places where people waited, but where people simply walked through, so they shouldn’t be leaving anything behind. But this doesn’t deliver the great promise that people imagine when they think of baggage left behind. They think of catching terrorists planting bombs in crowded areas, such as train stations or airports or bus terminals.

It’s such a tempting use of analytics, especially when it is so easy to demonstrate. It really isn’t that difficult to detect a bag being left in the middle of an empty platform. But when you start looking at the overall complexity of the application, how difficult it is to reduce false alarms, the problems with recognizing bags in crowded areas, and how easy it is for people to defeat the system by not leaving bombs in open areas, then you start to realize that this really isn’t such a great application for video analytics.

What is such a shame is that this application has gotten so much prime time attention, while the uses of video analytics that produce huge real-world benefits get much less discussion.

I think the big lesson here is the importance of thinking the whole system through. Detecting bags is the least of the problems. There are way too many bags and it is way too easy to plant bombs in areas that can’t be seen. The system turns out to be far more complex than it seems at first.

To counter my note of caution, I’d love to hear of examples where baggage-left-behind has been used effectively, and the key requirements needed to make systems like this successful.

John Honovich opened a discussion on this topic, along with a lot of good analysis at his web site:

http://ipvideomarket.info/report/sd_cards_video_surveillance_storage

This plays right into my most recent post on intelligent storage in the camera. The webinar I held last week had record attendance, so there seems to be interest in this subject.

I’ve been watching this shift in storage technology for the last few years, and I went through a lot of the same math that John spells out so well. I’d like to add a few added twists to the topic that show why I’ve come to a different conclusion.

The most important comparison that John points out is the cost of a 1 TB hard drive versus an 8 GB or 16 GB SD card. When you look at the cost in $/GB, the SD card is 21X more expensive. So, even though flash memory is dropping in price faster than hard drive memory, how could it ever catch up in the next five years?

Here’s an important piece missing from this comparison: You can’t just plug in your hard drive into the network, you need a storage server. When you get done adding the storage server, which includes all the hardware needed to manage lots of streams from many video cameras onto the hard drives, then the cost per TB is much, much higher. The typical end user price I’m hearing today is around $2,000. There is a wide range in pricing and options here, but this is the average I’ve heard.

That’s 20X more than the price of the 1 TB hard drive. Since the cost of the SD card is the total cost needed, this wipes out most of the cost difference with using SD cards compared to hard drives in a storage server.

If you put the hard drive in the camera directly, as we do in our iCVR, then you don’t need all of that server overhead. That’s why I think that today, putting a hard drive in the camera, ends up being the lowest cost storage solution.

For the next big factor that changes the math, take a look at this whitepaper by Uptime Institute:

http://www.uptimeinstitute.org/wp_pdf/%28TUI3008%29Moore%27sLawWP_080107.pdf

This shows a growing problem with the costs of data centers. It has become a lot more expensive to support servers these days than it used to be. Since the latest servers are far more dense in storage capability, and far more powerful, they also draw a lot more electricity to run, and they generate a lot more heat, which requires more electricity to cool the rooms down and higher support costs. On top of this, the numbers of servers in data centers are growing fast.

Take a look at Table 1 on page 3. It shows there that for every $1,000 you spend today on servers, there is a total cost of $3,510 over three years. That means you now need to pay the cost of your server over again each year, for overhead, power, and replacement of hard drives as they fail, in today’s data center. This is based on conservative electricity costs. In many parts of the world, it is going to be higher.

This includes the costs of hard drives failures, and that’s another big factor here, since flash memory lasts a lot longer. In fact, storage failure in cameras should become rare, while hard drives in DVRs and servers fail after 3-5 years.

While this overhead, electricity, cooling, support and hard drive failure costs aren’t usually calculated into the cost of servers when video surveillance systems are bid, that should change. IT managers realize this is a growing issue and that cost needs to be added into the true total cost of ownership when comparing.

These issues of power consumption and data center overhead, plus failure rates, is one of the reasons that solid state storage is expected to make serious inroads into replacing hard drives in storage data centers. The cost of the overhead and electricity and support has become bigger than the cost of the servers. Solid state storage uses far less power, has longer life expectancy, and can run at higher temperatures reliably.

The use of solid state drives is just now starting to be used in data centers, mainly for special applications, since cost is still higher. But most projections I’ve seen say that in 5 years, this is going to change and SSD will start replacing hard drives.

We can expect to see the same thing happening in laptops, since lower power consumption means longer battery life, and with the rugged abuse that laptops get, solid state storage makes a lot more sense from a reliability standpoint.

In five years, the growth rate of hard drives should start slowing significantly, as solid state memory starts taking over.

These are some big factors that clearly push the equation in the favor of SD cards as they drop in price, and they get large enough to provide 30 days of storage. They aren’t quite there yet.

The latest reports I’ve seen say that 64 GB SDXC cards will be available early next year, and 128 GB cards will be introduced by the end of 2010. If this is true, we could see 1 TB cards being introduced in 5 years, although I think it might take a year or two longer than that, since the rate of advancement will probably slow down a bit as the demand for such large storage cards will not be as high.

I agree completely with a lot of the other points that John raises: That video management software can’t deal with storage in the camera today. This needs to be addressed. Also, the storage on most SD cards in cameras cannot be accessed like you would a DVR (Mobotix is the only exception I know of, besides our own cameras with built-in storage). In fact, some cards need to be removed physically to access the stored video. So, this is a long way from ready for most cameras.

However, the cost impacts above suggest to me that SD cards in IP cameras (or microSD cards) will become the standard in five years. The largest megapixel cameras may require hard drives, but most of the cameras will have plenty of storage capability.

This is of course just a guess, and the thing about technology predictions like this is that you have to always wonder what factors you overlooked. Will a new technology be introduced that changes everything? Or does someone solve the problems with hard drives and server overhead costs?

Storage at the edge of the network, especially right in the camera, continues to generate interest, as I mentioned in my last blog post.

However, not many realize all of the reasons why it is so compelling.

For example, the whole idea for centralized recording of video comes from the traditional data center. It is natural to think that what makes sense for data centers is also the right solution for video storage.

It turns out, however, that this is not the best architecture for IP surveillance, which is why the industry has run into so many problems with bandwidth, storage costs, system reliability, etc.

Storing video in DVRs from analog cameras is actually closer to the edge of the network than IP cameras with server storage. IP cameras have actually been going the wrong direction. The answer that makes everything far simpler, and overcomes many of the biggest problems, is to go the other way and move storage and intelligence further to the edge, right into the camera itself.

It makes more sense to treat cameras as sensors, not just data sources, because sensing is what they are really about. This becomes much more obvious once you include video analytics in the cameras. Then it becomes much easier to see why sensor systems work better with distributed storage and intelligence. Where do you want to do the sensing? Back at a central location or right there at the sensor? Why send 100% of the video back, when you only access 1% of it? Why waste 100X as much bandwidth as you need?

I just finished a whitepaper that describes why intelligent storage in the camera is going to have a big impact on IP video systems. It’s a look at the future of where technology is going and why.

You can download the whitepaper here:

http://www.videoiq.com/products/resource-center/whitepapers/

I will also be holding a free webinar on the subject in a few weeks. Here is a link to register, if you are interested:

https://event.on24.com/eventRegistration/EventLobbyServlet?target=registration.jsp&eventid=153908&sessionid=1&key=BAB80E0D27E9D00175CACB0907737599&partnerref=july8&sourcepage=register

The leading video management systems companies are beginning to recognize the importance of intelligent storage in the camera, but I think it is going to end up being a much bigger trend than they realize. There are a number of future shifts in technology that are going to make it even more compelling. For example, solid state storage, megapixel cameras and the move to video web services.

Some have suggested that as bandwidth gets easier to find and storage gets cheaper that these will make centralized storage and intelligence more popular, but this is all based on thinking of cameras as if they were data sources, not sensors. Yes, there are lots of PC applications and data that run much more efficiently in the data center, but sensor systems work much better at the edge. Everyone has simply been looking at this the wrong way.

Video intelligence offers a lot more than just detection. It changes the whole system for the better.

I don’t mean to suggest that standard resolution CCD cameras are going to disappear anytime soon. But the industry reached some milestones in the advancement of CMOS image technology, and there seems to be consensus that CMOS imagers can now match even the low light performance of CCD.

The word I’ve heard from companies who work in the digital camera world is that as of 2010, all new digital cameras will be using CMOS imagers. Even in the highest end professional digital cameras, where quality is of the utmost importance. You won’t see CCD any more.

Digital cameras and camcorders drive most imager development, so, this makes 2010 a big watershed year for CMOS imagers.

Where this will have the biggest impact will be in megapixel cameras, since CMOS has big advantages on faster frame rates of megapixel images. CCD has always struggled with this.

The other big area is wide dynamic range. Post processing of CMOS images is the best way of getting ultra wide dynamic range – which provides a significant improvement in video quality, especially with outdoor scenes.

Another new development that is just starting to show up in very high-end digital cameras that is worth keeping an eye on, is post-processing of the image to improve low light performance. Nikon has a camera, for example, that can now take pictures with an ASA rating of 3,200.

You can’t always believe the ASA rating on digital cameras, but what Nikon is doing is adding a lot of extra post processing of each image allowing it to extract the image information out of the noise, making for much better low-light pictures.

The problem is that today the method they are using takes seconds to process one image, so it won’t work real time for video. But I’m sure it is only a matter of time before this can be done in real time, which will represent a huge advancement for security applications.

While I’m on this subject, I think it is also worth mentioning that CMOS imagers also passed another important milestone a few years ago. Industry experts agree that CMOS imagers have now passed the resolution and quality of film.

Here’s a link if you would like to read more about this:

http://www.normankoren.com/Tutorials/MTF7.html

While CCD technology hasn’t seen any significant advances in the last 5 years, CMOS imager designs keep getting better, going through improvements every two years. So, we can expect ongoing developments in the coming years.

The fact that VideoIQ’s iCVR includes a complete DVR in the camera continues to generate lots of great feedback. Integrators tell us that they love the way it solves problems, including bandwidth and storage, along with improved reliability and lower cost.

When we first introduced the iCVR, we called this feature “storage in the camera.”

Then we heard customers telling us that they saw other cameras with storage built-in. We heard about one that had 64 GB of storage – almost as much as ours. That was a big surprise to me, since I had looked hard to see if there was anything like our iCVR, but couldn’t find a single camera like it.

When I tracked down the camera with 64 Gb, it turned out to be 64 MB not GB, which is good enough to store a few video clips at best. Most engineers would not even call that storage, we’d call it buffer memory.

However, there are of course more cameras starting to show up with SD cards for providing storage in the camera. They don’t provide anywhere near as much storage as our iCVR, but they do store video. That’s when we started calling what we have a “DVR in the Camera”, hopefully to make it clear that what this means is something far more valuable than just a few hours of storage.

In other words, you can eliminate the need for an external DVR or an NVR with our iCVR, because the DVR is already built in. That’s what we meant by DVR in the camera.

Naturally, any good idea is something that others want to start claiming as well. That’s a sure sign of success.

http://www.sourcesecurity.com/new-products/listing/1/product-profile/cctv/image-capture/domes/mobotix-q24m-sec.html

As you can see, Mobotix is now using the term. They make it pretty clear that they’ve got a DVR in their camera. It’s not just storage, it is a DVR. But what do they mean? They ship their camera with a 4 GB card, which they claim is good for 4 hours of video. Of course, you can replace that with a 16 GB card, which would give you 16 hours of storage.

That’s not what we would call a DVR in the camera.

What we mean is I think what most people would expect: You have enough storage for 30 days of continuous recorded video or more. That’s what you need if you want to replace a DVR.

The only time you could eliminate the DVR with 16 hours worth of video recording is if you only recorded occassional event video clips, such as video triggered by an external sensor, or triggered by motion detection (provided it wasn’t outdoors – motion detection outdoors will be going off so often that 16 hours of recording won’t last long.) But if you only have a few video alarm events a day, and they each only lasted 10 minutes or less, then you could stretch that 16 GB for a month. Unfortunately, this isn’t typical.

Most projects require continuous recording. Why? For two big reasons: First, to make sure you don’t miss what is most important. Second, because it is often just as important to prove what happened as what didn’t happen. For example, someone claims their car was damaged in your parking lot at 2 pm on June 3. If your event video didn’t capture it, that doesn’t prove it didn’t happen, does it?

This means that none of these cameras that include a flash memory card for storage can replace a DVR, so they really aren’t a DVR in the camera. Don’t be fooled by the words.

Another thing to keep in mind with these cameras that allow flash cards: Some of them require you to physically go to the camera to access the video stored on the cards. It is great to have storage in the camera when the network needs to be taken down for maintenance or upgrades. But who is going to go around to all their cameras to collect cards to see what happened while the network was down?

Some of the better systems allow you to log on and see the video through your network, but even this is not ideal. You really want to have the video that gets captured in your camera in your DVR, just like any other video captured.

So, in this world of storage in the camera, there is a wide range of differences. They aren’t even close to all being the same. DVR storage in the camera is by far the best, because then you don’t need an external DVR, and this saves most of the bandwidth consumed by IP cameras. It also makes the system much simpler, and saves cost.

The latest chapter in this unfolding story was the recent announcment by Dedicated Micros.

http://www.securitysystemsnews.com/blogs/?p=2059

DM calls their “revolutionary” product the ICR. Sounds familiar doesn’t it? ICR stands for Integrated Camera Recorder. That’s pretty close to our iCVR, which means Intelligent Camera with Video Recorder.

But ICR doesn’t mean what you might think. Yes, it does include some storage in the camera (they claim 24 hours of recording), just like many other cameras with a flash card, but what ICR stands for is an external storage device that is built into their proprietary network switch. They have “integrated” the video recorder into the switch. There’s a full hard drive in the switch, so you can get 7 days of storage there, they claim. But apparently to get 30 days of storage, you still need a central server. This, of course, isn’t in the camera. It is just integrated to work with the camera over a point-to-point network connection.

What is great to see in all the marketing materials and promotion that Dedicated Micros has gone through is the way they are promoting how important it is to solve the bandwidth issues with IP cameras, and the many weaknesses of traditional IP cameras. Check out these links:

http://www.info4security.com/story.asp?sectioncode=9&storycode=4122273&c=2

http://vimeo.com/460935

I love Michael Newton’s forthright bashing of traditional IP cameras. I agree with a lot of what he says. So, I’m glad to see him educating the market on the problems with existing IP cameras and that there is a better approach.

What is still missing from their revolutionary new product, however,  is intelligence in the camera. Video analytics provide just as many benefits as putting the DVR in the camera. It significantly reduces the amount of video you need to store, and it allows you to capture high quality video whenver anything important happens, while recording everything at the usual lower DVR rates.

And of course the DM solution is still missing a real DVR in the camera, which means you don’t need any external recorders.

What I liked best in the DM presentation was that they called their ICR “revolutionary” and said there was “nothing like this in the market”. This was clearly a big announcement for them.

It is always nice to be copied. It is of course a true sign of appreciation and success. I expect to see many more going down this same path, because the benefits are significant.

But it is even better when the revolutionary products that come out still don’t even have half of what our iCVR has. That must make the iCVR “uber revolutionary”…

Right?

But I think the most important point here is that when new breakthroughs come along, it is natural to see others trying to use the same terms and trying to accomplish the same results, but don’t be confused by the words used. Dig deeper to understand what they mean.

Storage in the camera and a true DVR in the camera aren’t even close to the same.

If you want to read a lively discussion on storage in the camera, check out this from John Honovich’s site:

http://ipvideomarket.info/report/should_you_use_surveillance_cameras_with_built_in_storage

Doug.

The good news is that most high quality video analytics systems these days work quite well with IR lighting for night time detection.

The one thing that often gets overlooked, however, is that IR lights attract insects. Apparently they like the light and the heat. And insects often attract bats and birds, and sometimes even spiders, who occasionally spin a nice web across your camera lens to catch the bugs.

When you are just recording video, this only means you might have an annoying bug or bird flitting near the cameras. Not a huge deal.

But with analytics, it can cause false alarms.

I first learned about this many years ago. Our analytics have not had many reports of bug problems, but we’ve seen a few. Then, I ran across a report by Raytheon that summarized years of experience, including deployments in Middle Eastern deserts, proving the problems created by mounting IR illuminators too close to the camera. Their suggestion: Don’t mount IR lights near cameras.

Sounds like a good suggestion to me.

I just searched to see if I could include a link to the Raytheon study, but it doesn’t appear to be on the web any longer. However, I did run across this document:

http://www.dvmd.com/downloads/ApplicationGuide.PDF

This article says:

“The ring of LED’s around the lens of “bullet cameras” attract amazing numbers of flying insects in summer months.”

Bullet cameras with IR LEDs have become quite popular, but I’d suggest avoiding them with analytics. It is better to mount the IR lights away from the cameras and preferrably closer to the area you are trying to illuminate.

The article also makes this comment:

“Bugs can produce approximately a thousand nuisance alarm events per hour…”

We’ve never seen anything like that. In fact, false alarms are rare from bugs with any good analytics technology, but they do occur and so the wise thing is to avoid the issue by mounting the light sources at a distance.

The reason this article describes thousand of false alarms per hour is because they are talking about Video Motion Detection, not Video Analytics. This is just another example of why VMD is not reliable outdoors.

One other thing to keep in mind with nighttime detection: If you are using a day/night camera, you should know that some video analytics technologies have problems when the camera switches from day mode to night mode, or vice versa. When the IR cut filter switches in or out, it creates a sudden change in all the pixels. This can create false alarms for many technologies.

VideoIQ’s technology doesn’t have a problem with this, and I know there are other good video analytics technologies that don’t as well, but some do. So, if this is an application you are working on, you might want to check with your vendor first.

I first got intrigued with the issues facing City Surveillance after reading a study earlier this year that San Francisco published on the results of their video systems. I commented on this in an earlier blog post:

http://spotonsecurity.com/2009/02/06/san-francisco-surveillance-study-shows-need-for-analytics-and-real-time-response/

Then I began researching further and discovered that public studies by cities around the world were reporting almost identical results.

Now a new report just released by the UK Home Office with researchers from Sweden and the USA summarizes over 40 studies from around the world, and their conclusions are talking about exactly the same things that I noticed. Here is a link to that new study called, Effects of Closed Circuit Television Surveillance on Crime:

http://db.c2admin.org/doc-pdf/Welsh_CCTV_review.pdf

They show that traditional CCTV surveillance systems produce a small reduction on crime in some areas – on the order of 7% – but it has little or no impact on violent crimes and has its best results in parking lots and preventing property crimes.

What jumped out at me when I studied all of these reports was that they were missing the underlying causes of such disappointing results.

We’ve been seeing a significant growth in what we call Remote Guarding, where video analytics detect breaches of security or potential problems and send video clips to remote security personnel who are trained to respond through audio down to the site. This actually prevents the crime, compared with just recording it as in most traditional systems.

The results have been powerful in the commercial sector, for three basic reasons:

  1. The cost of pro-active monitoring is now practical for the first time. Where one person looking at a bank of monitors might at best keep an eye on 10-20 cameras, and do a fairly poor job of catching things, the Remote Guarding system that uses video analytics can have one person responding to 1,000 cameras or more and doing a much better job seeing potential problems.
  2. Traditional systems have not been able to respond quickly enough, even if they can detect a crime in progress. The time it takes for police to get to the scene means that they are often there too late. This is one reason why existing systems have had almost no impact on violent crimes. If you can have an instant response, you can stop a dangerous situation from escalating. This is far more powerful than mopping up after the murder or attack.
  3. The quality of the video was often poor because frame rates were too slow and resolution was scaled back in order to reduce bandwidth and storage costs. Storage in the camera and using analytics to intelligently control the recording solves these problems, as I’ve mentioned before.

These are issues that we’ve been solving in the commercial sector with Remote Guarding for the last few years, and are exactly the same underlying issues cited throughout all the city studies I read.

In other words, Remote Guarding using video analytics for detection and IP audio for response has the potential to transform city surveillance.

The commercial sector is seeing 50% to 75% reductions in costs compared to on-site guards, while improving protection and reducing crime dramatically. There is no reason I can think of that this same formula won’t have the same impact on preventing crime in our world’s cities, public parks and hotspots.

Security Systems News just ran an interesting article talking about the growing interest in private monitoring of public places:

http://www.securitysystemsnews.com/?p=article&id=ss200905c7kwET

In there, you’ll see the first case of Public City surveillance using Remote Guarding. Birmingham, Alabama, has contracted with ION Interactive, who has deployed VideoIQ’s iCVRs throughout the city, along with some commercial locations.

Richard Cruit, the COO of ION Interactive,  and I just ran a webinar this last week, sponsored by SDM Magazine, talking about the results in Birmingham and how Remote Guarding can have a significantly positive effect on City Surveillance.  You can watch this webinar from SDM’s archives by going to this link:

https://event.on24.com/eventRegistration/EventLobbyServlet?target=registration.jsp&eventid=144275&sessionid=1&key=CF33F1A5642D363ED9BDA7202D2EBFD8&partnerref=website&sourcepage=register

It is time for a new approach to city surveillance and how to use video to reduce crime. The technology is here, and the results are coming in every day that prove how effective it is. With all the money being invested, we shouldn’t be settling for small reductions in crime, when we can make our video systems pro-active and help break the cycles of crime before they happen.

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