For example, we never had to worry in the past about how much coverage we could get from a camera. No one used to mention pixels-per-foot (or per meter) ten years ago. Why? Because when we wanted to see up close, we used PTZ (pan-tilt-zoom) cameras to zoom right in on the person’s face or their license plate. With 20X zoom, you had all the detail you needed.

But PTZ cameras need someone driving them to get those close-ups. Megapixel cameras offer the ability to have a fixed camera that can record enough detail. You can zoom in later, or in real time, without moving the camera.

In many cases, that is a lot better. Unfortunately, there just aren’t enough pixels to do all the zooming we do with a PTZ camera. You need 400-600 million pixels to get the same 360 degrees of coverage with 20X zoom that you get with a PTZ camera. That’s 100X more than even the large 5 megapixel cameras sold today.

So, we need to figure out how much coverage we can get with megapixel cameras. Life just got more complex.

John Honovich provided a great service to integrators with the testing he did to show how many pixels per foot you need to get good detection. See his article here:

http://ipvideomarket.info/report/pixels_surveillance_video_test

However, as he points out, you can’t reduce it down to one single number. It depends on many factors.

But here are a few rules to make this simpler.

First, remember this important fact: There is a big difference between general surveillance and trying to recognize a person’s face or their license plate.

Facial and license plate recognition require about 10X – 20X as many pixels per foot, and you need special lighting, plus a number of other factors to keep in mind. Studies have shown that even close up pictures of someone who has changed their hair color or beard is hard to confirm for certain. But it requires less resolution to recognize someone you know. This makes recognition m0re difficult to design for, especially if you want to use the evidence in court cases where jurors never know the people in the videos.

General surveillance, however, where you are only trying to detect if people are in the area, or how many people there are, is much simpler. John’s study shows that 5-12 pixels per foot should be enough. Our testing at VideoIQ shows that our video analytics match this number, which means that our analytics are just about the same in their ability to recognize people in an area as people watching a monitor.

Actually, our analytics might be slightly better, since we recommend that 5-8 pixels per foot for most conditions. This means that if people are too small to see, or the lighting is too dim to see them on a monitor, then video analytics are probably not going to accurately detect them either.

Not all analytics technologies are this accurate. Some require more resolution. More importantly most analytics systems don’t analyze all the pixels. You will find that systems generally scale the video resolution down to minimize processing power for analytics, so be sure to check the actual resolution they are analyzing.

A good second rule of thumb here is: Good video analytics require about the same pixels per foot for accurate detection as humans watching a monitor.

To make life simpler, also remember this: Horizontal pixels determine coverage.

This means that knowing how many pixels are on the horizontal axis, and knowing the pixels per foot for good detection, you can calculate the coverage of a camera. This simplifies estimates. Below are some standard resolutions calculated, based on 5-8 pixels per foot for general surveillance:

CIF (357 horizontal pixels) . . . . . . . . . . . . 45 – 70 feet of coverage

D1 or 4CIF (704 horizontal pixels)  . . . . . 90 – 140 feet of coverage

1.2 MP (1,280 horizontal pixels) . . . . . . . 160 – 250 feet of coverage

1080p (1,920 horizontal pixels) . . . . . . . 240 – 380 feet of coverage

3.1 MP (2,048 horizontal pixels) . . . . . . . 250 – 400 feet of coverage

5 MP (2,592 horizontal pixels) . . . . . . . .  320 – 510 feel of coverage

What is surprising is that the above numbers are true no matter what focal length lens you use!

A 3 mm lens will give you good detection up to about 100 feet away, while a 10 mm lens will work to around 300 feet (for a D1 resolution camera), but in both cases you still have the same horizontal coverage. So, you can copy the above numbers and use them for your planning estimates.

A couple things to keep in mind:

1. Interlaced video reduces horizontal resolution. The latest studies I have seen suggest a 25% reduction in horizontal resolution. So, be sure to reduce coverage numbers when cameras are using interlaced video (1080i means it is interlaced, while 1080p is progressive, and almost all analog cameras use interlaced video).
2. You need lenses with the capability of capturing the full resolution of the imager, or you aren’t going to get the full horizontal coverage. This, unfortunately, is a common problem with many megapixel cameras shipping today.

A few interesting observations emerge from the above chart:

• Even though a 1080p camera only has 2.1 megapixels, it has virtually the same horizontal coverage as a 3.1 MP camera. That’s because of the 16:9 aspect ratio of HD video.
• A 1080p camera has almost 3X as much coverage as a standard resolution camera (D1 or VGA), but a 5 MP camera only has about 30% more coverage than a 1080p (and you will only get 30% added coverage if you have a true, high quality, 5 MP lens).

You can see why 1080p looks like the resolution that the industry seems to be moving towards.

Even though there seem to be  way too many resolutions of megapixel cameras to choose from, and way too many numbers to keep in mind, things are getting simpler.

PTZ cameras were once king of the hill. They represented the best possible technology you could get; giving you the ability to see in every direction  and zoom into the smallest details.

Companies like Pelco, Vicon and Kalatel, to name a few, started their businesses by producing high quality pan, tilt, zoom cameras. They rode the wave, as PTZs defined video surveillance, and grew into leading video companies.

But the problem is that all the power and benefits PTZs deliver are only gained when a person is sitting there actively panning, tilting and zooming. With only a few percent of cameras being actively monitored, and only a few percent of those being watched at one time, there is no one at the helm more than 99% of the time.

The other disadvantage of PTZs is that the moment you’ve zoomed in to see a license plate or to get a close-up of a person’s face, you lose the ability to see everything else. You can easily miss something more important.

If you want to look across a site, PTZs are valuable tools. But if they are sitting there, not being actively driven most of the time, they are expensive cameras. That’s why many PTZs are set for continuous tours, where they move from one preset location to another, auto-panning. This also partly overcomes the problem with missing things when it is zoomed into one area. They move from one point to another to cover a wider area. This allows one expensive camera to cover the whole area. That’s the hope, anyway.

However, belts, gears and motors don’t last long when the PTZ is set for continuous tours. The best quality models need replacement parts every year, when set for auto-panning. Cheaper model PTZs wear out even faster. When that one expensive camera fails at the site, you’ve now got nothing.

Megapixel cameras have taken a big chunk out of the once powerful PTZ. No motors, belts or gears to wear out. Even when you zoom in, you can still keep on recording the whole scene, so you won’t miss anything. This is ideal for recorded video. Megapixel cameras don’t give you anywhere near the full zoom capability of PTZs. You are limited to about 3X-8X for a megapixel zoom, not the 20x-30x you get with a PTZ, but in many cases that’s fine.  And, you can buy two megapixel cameras for the price of one PTZ.

Video analytics are also changing the world of PTZs. The applications where PTZs are most important – were live monitoring is needed – that’s exactly where video analytics provide the biggest bang. They enable security personnel to monitor far more cameras much more effectively, by proactively popping up cameras when the analytics see potential threats.

Why not use video analytics with PTZ cameras? You can, and we sell such systems all the time. But if you want to be sure you are going to catch an intruder, you can’t have that PTZ panning all over the place. Fixed cameras are the only way to assure you never miss a threat.

For example, let’s say you have a site where you want one PTZ camera to auto-tour across four different preset locations. The PTZ would move to one preset, watch for some time and then move on to the next. How much time will the camera spend watching any one area? Less than 25% of the time! That means more than 75% of the time you have no visibility on what is happening. With more than four presets, it is even worse. That’s a huge blind spot in your protection!

The alternative: You can put up 3-4 cameras with video analytics built-in for about the same cost as a PTZ camera with analytics, and you won’t have yearly replacement costs for the motors, belts and gears. Most importantly, the analytics won’t miss what is happening – so you get much better security. In fact, it is the only way to go, when you need surefire protection.

In other words, fixed, non-moving cameras with analytics now give the best site awareness. With active video analysis doing the watching for you, it is better to have more fixed smart cameras than a panning PTZ.

So, the new king of the hill is a megapixel camera with analytics built-in. This gives you zoom, a wide area of coverage that is never missed, and analytics to detect potential threats so that you know which camera to be looking at.

Even better, add a PTZ camera at the site if you want the ability to zoom in for a close-up. That’s where the PTZ excels. It’s a tool for extreme close-ups. This combination system has persistent protection because the megapixel cameras continue watching the areas they are trying to protect. They never look the wrong direction. Plus, the analytics watch for threats continuously, even when no one is watching the monitors.

The PTZ will be around for a long time and still plays an important role in surveillance. However, it is not quite as important as it once was. It once was king. Now it is a good soldier. The world has changed for PTZs.

IMS Research placed storage in the camera at #2 of the biggest trends for 2010:

http://www.imsresearch.com/press_release_details.html&press_id=1224

Here’s a link on a recent review at the latest industry trade show in Las Vegas, last month:

http://www.experteditorial.net/securitysquared/2010/04/when-inteligence-moves-to-the-edge-1.html

As you can see, there is a lot of talk about storing video in the camera as a growing trend, and many of the leading companies are getting on the bandwagon. But what isn’t being said is that in almost all these cases, what they are talking about is not the ability to store enough video in your cameras to replace the DVR or NVR in your system.

What they are talking about is simply buffering the video storage. That means storing just a little bit of video, such as hours worth or sometimes a few days worth of video.

Anyone playing video from the Internet uses video buffering. That’s why it says “loading”, since it is creating a buffer in your PC, so that it can play the video smoothly without interruptions. That’s a buffer, not long term storage. You can’t go back the next day to find the video file on your PC, because it is erased. This is the difference between storage and buffering.

There are two main benefits for buffering video in surveillance: First, if your network goes down for a few minutes or a few hours, you don’t lose the video from your cameras. As long as you don’t wait too long, you can go back to that buffered video to see what happened during the network downtime.

Second, when transmitting video through networks, there can be short interruptions or delays. This is especially notorious with wireless networks and the Internet, which is the same reason video streams are buffered from the Internet. Buffering the video helps a lot in overcoming these kinds of issues.

But this is completely different from true storage in the camera, where you can get months or even up to a year’s worth of video storage right in the camera.

I hear strong interest for storage in the camera, since it eliminates the need for an external DVR or NVR, and it also reduces 99% of the bandwidth used by IP cameras, since most of the bandwidth is used while streaming video across the network for storage. Only about 1% of security video is actually used, so that means 99% of the bandwidth is wasted.

There are also in many cases significant storage cost savings, when it is built into the camera.

To get this kind of storage, we at VideoIQ include a full hard drive in the camera. That gives enough for months worth of continuous video storage. In fact, we now have models with up to a year’s worth of storage.

However, I rarely hear much excitement over buffering video. Most integrators realize that losing video during network down-times is a problem, but few want to pay the extra money for this, since it rarely occurs.

So, it looks to me as if most of these companies are jumping onto a bandwagon without realizing what they are jumping onto.

There is one application where cameras with a little bit of recording can make use of the storage in the camera: For applications where the camera is not recording continuously, but only with motion detection or some other kind of detected event. If events are rare, you can get by with a little bit of storage.

But the problem is that motion detection rarely works outdoors, since it cannot distinguish all the pixel changes occurring from changes in the light, trees blowing in the wind, etc., that are happening all the time. So, it will end up recording most of the time, which means you need a lot of storage. That’s why video analytics are needed – they can recognize what a person looks like, for example, and ignore all the kinds of pixel changes and movement in the scene.

One last thing worth mentioning on this subject that is rarely mentioned: Most of these companies selling cameras with what they call storage have no easy way of getting the video off the cameras. In some cases, if you want to retrieve the video, you need to go around to all of the cameras and manually collect the SD cards. Who would ever use such a system?

Frankly, I don’t think anyone is using such systems, but they are being advertised as storage in the camera.

There are a few companies who have software that enables you to access that buffered video, but there hasn’t been support for this with the large Video Management System companies (VMS). So, in large camera systems, where a VMS is usually a requirement, you are up a creek without a paddle, since there is no way to use that software to access the stored video.

Fortunately, this is changing rapidly. Genetec just announced a new update to their software that includes edge storage support. Milestone is going to be releasing something soon. OnSSI is also close.

One company not mentioned in the above video, but who is clearly way ahead of all the other VMS companies in this category is IP Vision Software. They’ve designed a system that was designed from top to bottom for distributed storage. So, they have full integration of storage in the camera capability. They are definitely worth checking out.

So, when is storage in a camera not storage?

First, when it is only buffer storage, for a few hours or days worth of video. It may help cover network failures, but it won’t replace the need for an NVR if you want continuous recording.

Second, when you have no easy way of accessing the video. It should be just as easy to playback video in the camera as it is from your NVR or DVR. It should be no different.

It was eye-catching. Lots of people stood there staring at the analytics detecting people, cars, trucks, motorcycles, sailboats, speedboats, etc. Here’s a picture:

Unfortunately, this blog can’t show the full resolution or video, which you really need to see to appreciate how incredible it looks.

When I say this is the first public display of “true megapixel analytics” I mean the resolution being analyzed is megapixel. There have been cameras with megapixel video that have had analytics processing before. CoVi is a good example, may they rest in peace. They sold a 1 MP camera that ran ObjectVideo analytics. However, the resolution of the analytics was only CIF (320 x 240 pixels), which gave hardly any detection range. It was silly to put CIF analytics on a megapixel camera.

Why hasn’t anyone ever demonstrated megapixel analytics before? Because of the sheer processing power that other technologies need to do this.

VideoIQ’s technology is different. We need about 1/8th the amount of processing power compared to other high quality analytics systems. So, we can run the whole thing in one of the popular low cost DSP processors. But all other analytics technologies need a lot more horsepower.

For example, ObjectVideo on their web site claims they can run up to 4CIF resolution video in the same DSP chip we are using. However, in most cases the users of OV onboard are only running CIF resolution, because there are serious limitations running 4CIF, such as only being able to have one rule running at a time and a limited number of objects that can be detected.

IOimage uses two DSP processors in their cameras to get high quality and avoid compromising detection.

The camera we demonstrated was a 1080p camera, which is 1920 x 1080 pixels. We demonstrated it live at the show, with the analytics all running in the camera. It provides 3X the horizontal coverage of a standard resolution camera, and more than 2X the anaytics detection distance.

For other technologies to run 1080p analytics, they would need more than 6 times as much processing power, compared to 4CIF video. That would mean 6 DSP chips, or some very expensive high end DSP chips.

If you try to run this on a server or a PC, you would need a full dual core processor to run one camera. So, you can see why it’s never been shown before. It is impractical for other technologies.

The other industry first we showed is something we call IQTrack. It uses the video analytics to automatically track and zoom on objects in the field of view. Here’s a picture:

This is different from PTZ camera tracking. If you look at the lower left of the picture, you will see that the whole field of view is still being recorded and it shows where in the scene you are zoomed into. So, you can always go back later and pick another part of the video to look into.

The other unique thing is, if many people are in the area, you can click on one person and it will zoom in on and track just that one person. That’s never been shown before either.

Watching it, you can immediately see that there is no comparison between watching video that is automatically zooming and tracking on important objects, versus static video cameras. It pulls your eyes to exactly what is important. I think this is going to be very popular for megapixel cameras.

The 1080p cameras we sold will also be the first cameras to ship with a new imager from Sony that has some amazing low light performance. We are still testing it, but it looks to be 2X-4X better than any other multi-megapixel imagers used in the security industry.

And of course, the camera we showed included a hard drive so that you can store 1-2 months of high quality 1080p video. This solves the bandwidth problem for megapixel cameras, since it needs no bandwidth to record, and eliminates the need for external storage in most cases.

Now that true megapixel analytics have arrived, I think it is going to set the standard, and I think it offers incredible visual value to megapixel cameras, even if you don’t want the analytics for detecting alarms.

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?

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.

They gave us excellent feedback, which we’ve used to make the iCVR better.

Besides the extremely positive responses, however, we heard something that surprised us: Almost all of the CSOs we talked to said that they would love to put up more surveillance cameras, but they were concerned about the liabilities. Could the iCVR reduce the liabilities inherent with video cameras?

What they were referring to is the potential lawsuits that can arise when a camera is installed, if it isn’t monitored. The public can see the camera and imagine that it is being watched. If something should happen, they expect a response.

The problem is that less than five percent (5%) of surveillance cameras are monitored today, because it has been too expensive to have people watching cameras all the time. The general public doesn’t realize this, however.

The CSOs weren’t raising a needless concern. They could each recite the lawsuits that had already proven this is a real problem. They could tell you how big the settlements were for.

Apparently, there are a number of cases where the courts have ruled that when people see cameras, there is “a reasonable expectation of response.”

In other words: Yes, there is an increase in liability for any cameras you have installed that aren’t being monitored.

These CSOs were from the Fortune 500, so they knew the danger of increasing their company’s risks. However, they also knew that adding cameras could make their properties safer for employees and customers. So, they weren’t happy about not putting up cameras. In many cases, they accepted the risk simply because they felt safety and security was just too important.

The minute they saw the iCVR with its built-in video analytics, they saw it as a potential boon for increasing protection without increasing liabilities. They could each think of a dozen locations where they wanted to add cameras if they could solve the liability problem.

This is just one of many examples showing how video analytics are changing the equation for security.

The cost of monitoring, which can now be managed remotely from anywhere in the world, has been reduced by 90% or more with the iCVR. One person can now monitor up to 1,000 cameras, and do a much better job.

So, a person sitting in one office, for example, can monitor the cameras for all of their company’s buildings at the same time. And if they use audio over IP, they can respond immediately to prevent a crime or defuse a situation. Or, they can contract with a number of Remote Guarding companies who are glad to offer this service.

Yes, when you do have monitoring, you do indeed reduce your liabilities for the cameras you have installed, because you can respond. This improves the safety and security for your employees and customers, as well. And yes, the iCVR makes it cost effective to both monitor, and using audio you can respond immediately.

The iCVR was especially designed for Remote Guarding, thanks to feedback we got from the CSOs.

If you want more info on this, check out: www.remoteguarding.org

The amazing power of a story is how it creates images so striking that people see life through that lens. Many will even see it that way when it isn’t true. George Orwell created such a vivid picture with his book, 1984. So, today, we find people seeing Big Brother, or the fears of Big Brother, all the time.

However, there is a much bigger trend going on today that never gets mentioned: Little Brother.

Look at all the cases where people carrying cell phone cameras or camcorders have caught government officials or politicians crossing the line.

Which is the more powerful change taking place? It is clearly Little Brother.

Why is that? Because, as surprising as it might seem, technology empowers the little guy more than it does government or big corporations. It gives more power to the individual.

Back in the wild west days of America, they called Sam Colt the Great Equalizer, because the revolver by that name could take the big land tycoon and make him very equal to a single person. The invention of the gun equalized the power of established authorities.

This is exactly why democracy has grown hand in hand with technology. And this is also why we see the rise of terrorism in the world.

Terrorists can only exist when there is technology that can put the power of widespread destruction in the hands of a few. This is proof that Little Brother is the big force to be faced in the future. But all we ever hear about is Big Brother.

I hate to say it but Big Brother is more like the endangered species. It is getting harder and harder to find kings these days.

But all of this just shows the gap in perception that can come from these lenses created by a culture.

Take the article that just ran in the Boston Globe about Intelligent Surveillance:

http://www.boston.com/business/technology/articles/2009/02/08/surveillance_gets_intelligent/

It talks about using video analytics for intelligent detection and Remote Guarding.

But what is just as interesting are the responses. Read them below the article. Or you can see them on this page:

http://people.boston.com/articles/abusiness/?p=articlecomments&activityId=5896397118473455799

Big Brother shows up in the first post we see. The second person sees security professionals as a protection racket. These are lenses that come from the images presented in the media and in movies.

Later on a few professional security people added their comments. They see this new technology as a big benefit:

“This technology is needed. Unmonitored cameras have been proven to NOT deter crime. This company is actively watching cameras and making it known by speaking from them. This is real security vs. false.”

Spot-on!

exChiefofPolice said:  “In an ideal situation I too would like to see more “boots on the ground”. Unfortunately, that is not realistic economically.”

What’s even more interesting, but I’ve never heard anyone mention this before, is the way that video analytics will actually reduce the problem of people watching things they shouldn’t. We know that there are cases of people monitoring who pan the cameras to follow an attractive face, and we all know that this is exactly the opposite of what we want such equipment to be used for.

But analytics eliminates the need to be panning and looking for a problem. In fact, those who try to use video that way just about go nuts trying to watch and look for something that isn’t happening. The human brain wasn’t built for that. No wonder they try to find anything of interest to keep looking at the most boring video you can imagine. What people do very well, however, is respond when something happens. Assess the situation and knowing what to do.

So, you set up the rules for what you want to be notified about, and that allows people to review exactly those situations and respond. This makes it easy to define what people are watching.

This means that it will be easier to regulate and control how and where video is used, while at the same time providing much better security protection for everyone – especially the little guy.

In the future, I expect that video analytics will be able to extract the image of the person and will be able to encrypt it, so that monitoring folks can’t see who it is until a law has been broken or a crime committed. Then you will be able to unencrypt the video to show who it is. This will provide even more privacy.

Technology is not taking away the power of the little guy, it is making us all more powerful as individuals. Technology also makes it easier to regulate and control the proper use of technology.

However, along with all of this improvement in our lives from technology comes those who would use it for personal gain. Terrorism is going to continue to grow as technology grows. It is Little Brother that is the bigger threat in the future than Big Brother.

That’s how it looks to me, anyway, when I step way back and look at our world with a wide angle lens.

Facial recognition, for example, had people trying to find faces in a crowd from a watch list shortly after 9/11, which was completely beyond its capability at the time. The technologists knew this, but some companies still encouraged projects like this, creating a serious negative reaction.

We’ve seen disappointment in some applications with video analytics, such as baggage left behind detection in an airport, which is far more complicated to make work practically than is generally presented. We at VideoIQ have avoided even offering it for this reason.

This makes education important. The good news is that most in the industry are trying to set better expectations on what is realistic.

However, there is one area that is shockingly out of whack.

Of the five largest camera companies in the security industry – all well known names by any integrator – except for Axis, they all claim to offer Video Analytics options with some of their IP cameras.

Most people assume this means that they are offering the kind of detection and performance as the largest and most well established names in the Video Analytics space. Technologies like VideoIQ and other early entrants were, after all, the ones who established what Video Analytics means to the industry. However, the software these camera companies provide and the advertisements they run suggest that they have the same capabilities. It is one of the biggest lies I’ve seen in the industry for a long time.

I can certainly see why they would want customers to believe they are offering products in the same class, but the technology isn’t even close. And it is clear that they know it, since they not only use the term Video Analytics when referring to their technology, they also call it Intelligent Video Motion Detection, or Adaptive Motion Behavior, or Motion Detection, etc.

What they are really offering is what is known as Advanced Video Motion Detection, and this is dramatically different from true Video Analytics.

For example, none of these AVMD technologies can distinguish a human or a vehicle from anything else, except by size. In other words, they only recognize a blob of moving pixels, and if the size of that blob is about right then and only then will it be detected. This falls far short of true object type detection.

Secondly, these technologies cannot work accurately in scenes with highly dynamic backgrounds, such as blowing bushes or tree branches, or rippling water: In other words, the types of changes and movements you find in typical outdoor applications.

You have to dig deep into their manuals to find the truth. Here are warnings from one of the big five:

• Movements may falsely be detected if there is: a reflective metal background, glass (glazed building frontages), water as a background
• Large areas of reflected light can also cause spurious motion detection
• A constant background is necessary in order to detect motion reliably
• A person walking front of a hedge that is moving in the wind will very probably not be detected

Another of the companies added these warnings:

• The ideal scene selection is one with light traffic and a clean background
• If heavy traffic or a busy background is unavoidable, place the monitoring zone or trip wire in a relatively stable area
• Avoid crowded scenes where people move in all directions or stand in one place for long periods of time

One of the top camera companies offered no warnings at all. However, the largest camera company in the industry gave the best list to show the limitations of Advanced Video Motion Detection. They say their technology might not work if the:

• Camera is shaking.
• Depth of object is too long.
• Object is too big or too small.
• Fluorescent light is flickering.
• Too many objects are moving.
• Weather condition is extremely poor.
• Movement of object is too fast or too slow.
• Object is moving directly toward the camera.
• Dirt, drip, or splash is on the dome cover of camera.
• Luminance level of image is too low (During nighttime, etc.)
• Outside light (sunlight, headlights, etc.) enters the shooting area.
• Luminance level of shooting area is subject to change (outdoors, by the window, etc.)

Real Video Analytics technologies can work under all of the conditions listed above. There are of course limits, and there are still false alarms, but you will see about 10X – 100X times as many false alarms in typical outdoor environments with AVMD, and many times more missed detections as well.

Advanced Video Analytics systems are designed to:

• Detect colors, contours, shapes and movements of humans and vehicles, not just luminance levels
• To ignore fluorescent lights flickering, sunlight, headlights
• Work in bad weather: Rain, snow, hail, sleet, fog
• Ignore camera shaking, dirt and drips on dome or lens
• Adapt to changes in environment automatically

If used indoors away from windows, a good AVMD system should work fine. Motion detection has always been usable indoors, and the new Advanced Motion Detection systems are slightly better. However, outdoors is another story.

These companies should be up front about these limitations, and they should be making it absolutely clear that AVMD is not in the same league as the technologies from VideoIQ and other true Video Analytics providers.

The problem with all of this is that the term, Video Analytics, is not being used carefully. People are using it to describe everything and anything, including traditional motion detection, license plate recognition and facial recognition. My suggestion is that industry start clearly differentiating: The term Video Analytics should only be applied if the system can provide:

1. True object type recognition, not just pixel blob detection
2. Able to discriminate objects of interest from highly dynamic background movement
3. Automatically adjusts to changes in the environment
4. Can track objects through the field of view

If it can’t do all of these, then it should be called AVMD.

The terms, License Plate Recognition (LPR) and Facial Recognition (FR) are clearly recognizable. There is no need to lump these into the term, Video Analytics. It only confuses what Video Analytics stands for, and leaves us with no clear way of distinguishing the type of system described above. Clarity is important, and so is honesty.

What is fascinating about this lie is that it isn’t the Video Analtyics companies who are perpetuating it. Usually you find the start-up companies with the breakthrough technology who get carried away, hoping for more attention. But in this case, the biggest camera companies in the industry (except for Axis) are the ones trying to ride on the coattails of the Video Analytics leap forward.

There is no excuse for not making these differences clear, and no one should be trying to make it look as if they are selling Video Analytics when they aren’t.

• Solid state imagers were getting better and better, and less expensive.
• Video compression was making huge strides. You could even send video over a phone line back then!
• Hard drives were storing more and more video in leaps and bounds.
• And of course, Ethernet and the Internet were started to change everything.

Studying these trends, it looked fairly obvious that video was going to become far more useful and more powerful than ever before.

I’ve always tried to simplify complex changes to help see where things were going. So, in the mid-1990’s, I mapped out three major phases that these changes would go through:

1. Digital Video Recording – the shift from VCRs to DVRs

2. Networked Video Systems – the shift from analog cameras to IP cameras

3. Intelligent Video – the shift from dumb video to smart video

When guessing and forecasting where markets and technologies are headed, the most interesting question to always ask is: Where is the value? That will reveal the biggest driver for change.

In the case of video: It shows you what is happening and what happened. This provides significantly more information than other alarm sensors ever could. Seeing helps you provide better protection for people and their property.

Then, what would happen if you could extract information from the video automatically? What if you didn’t need someone looking at a monitor to watch what was going on?

This is how Video Analytics increases the value of video. And this is why the last of the three above phases will end up being the biggest. When all that valuable information locked in the video files can be freed, you change the way video can be used. You can share that information with other systems, and people around the world can respond immediately.

I haven’t seen any blogs investigating Video Analytics in depth. There are a lot of misunderstandings and some bad information, which is common with any new technology. And big changes require looking at things in different ways than before.

So, I hope this blog helps you learn how to take advantage of this new technology shift, and avoid the pitfalls. There is no question about the impact Video Analytics will have. How will it change the video system of today? That’s one of the interesting questions this blog will be exploring.