Video analytics

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October 29, 2010

It’s Not Always Logical, When It Comes to Analytics

Posted by Doug Marman under Analytics accuracy, Video analytics, VideoIQ
[4] Comments 

We continually see integrators and users getting into trouble with their video analytics choices because they thought they were making logical choices. What seemed reasonable turned out to be wrong. Unfortunately, this has led to a lot of disappointment.

For example, here are some of the common mistakes made, that seem logical, but often turn out to be false:

  1. Having more manual control over how the analytics work will give you better detection accuracy.
  2. The longer the list of analytics behaviors, the more advanced and better the system is.
  3. The company that is known best in a market will have the best technology, or at least very good technology.
  4. Calibration adds accuracy, and therefore such systems work better than those that don’t require calibration.
  5. A system tweaked and tuned by a well-trained technician or engineer will work better than another system installed by someone with little training.

All of the above seem to make sense. They certainly would not lead to big problems if you used that kind of reasoning when choosing most technologies or products. However, they fail when it comes to video analytics. Why?

Let’s review the above 5 cases. Then the answer will become obvious.

1.  Most analytics systems require tuning and tweaking to reduce false alarms. Performance is horrible without those knobs and adjustments. However, this is actually a weakness, not a strength. When we introduced our first VideoIQ products, back in 2003, we had a system that worked fairly well out of the box without any adjustments, but we included some calibration tools as well, to help improve detection accuracy further. What we soon discovered, however, was that calibrating the size of objects often created more problems than it solved. Our biggest users stopped using the calibration and got better results. In other words, it is easy to make things worse, and it can take lots of training to know when to use which knobs. In 2007, when we relaunched the technology, we made the calibration process fully automatic. This has been a huge advantage and leads to far more successful deployments than we’d seen before.

This doesn’t mean that having added controls is a bad idea. They can, if used correctly, help. But this doesn’t mean a technology with lots of tuning knobs is better than one with less. From what we’ve seen, the opposite is true.

2.  Generally, a long list of capabilities is a good thing, even if you don’t need them all. Comparing products, why shouldn’t you choose one that can do more? When it comes to analytics, the reason is simple: Because it is far more important that the product can work well enough to be usable. For most video analytics systems, this simply isn’t true.

We’ve seen serious money put into offerings by big name companies, that have long lists of behaviors and some beautiful software, but they are disasters in the field. Why would companies spend so much money, and risk their reputation, to offer a solution that was not even usable in most cases? Because they are following other market leaders. This is the blind leading the blind.  It might look like it works in a lab, but the real world is far more difficult. Don’t worry about a long list of behaviors. Look first for something that really works  – at least well enough to be usable. That’s hard enough to find in the analytics space today.

3.  The company who has had the best market awareness is ObjectVideo. Unfortunately, they have also created more problems for the industry than anyone. This is just about unanimously agreed on by all the video analytics companies I’ve talked with. Their technology has serious problems with detection accuracy. Even ex-employees of their company have admitted this to me. They achieved such wide attention, not from so many happy customers, but from having raised $60 million dollars early on, and using those dollars to market and promote themselves into the industry recognized leader. Their sales grew rapidly, but then plummeted as problems became rampant. Seeing the problems that have been created, we’ve taken the opposite approach. We  spend hardly any money on advertising in publications, because we know that people first need to see it work. So, we rely largely on word of mouth, and helping people try our products. As far as we know, we are by far the fastest growing analytics solution on the market.

4.  I’ve blogged about the problems with manual calibration already, so I don’t need to go through all of this again. Calibration does improve accuracy, but the problem is that manual calibration also introduces problems. Automatic calibration that continually adapts as the scene changes over time, is better. But this isn’t the most important issue. The underlying accuracy of detection is far more important. While calibration does improve performance, the question you should ask is: Where is the accuracy starting point, before this improvement? How well does it work without any calibration? That’s far more important when comparing technologies these days. Why? Because most are horrible without calibration, and it shows how bad their underlying accuracy really is. Calibration is only going to filter out a limited number of false alarms. Try this test when comparing video analytics: Run them without any calibration and see how they compare. It’s a real eye opener.

5.  The logic for this one is simple: Installing a system by a well-trained engineer should always perform better than when installed by someone without any training. Seems obvious. So, then, buying products that will be installed by an engineer from the factory should be better than systems installed by integrator who are not experts. The logic fails, however, because the underlying accuracy of the technology is far more important. All the filters and tuning knobs and calibration can only patch up so many holes. We saw one system, with over a hundred cameras, where factory engineers spent months tuning and optimizing the system, but it still produced 10 false alarms per day per camera. That was an improvement, since it started off producing twice as many when it was first installed. As a comparison, they took our cameras and installed four of them in the worst locations. They produced 0.5 false alarms per day. This was without any tuning or tweaking.

Simple logic seems to fail because there are way too many companies selling what they call analytics, but they aren’t even close to being good enough. John Honovich, who has been making a concerted effort to test as many video analytics systems as he can, recently posted in a discussion with me that he was coming to the conclusion that out of 40-50 products on the market, maybe 3-4 at most were usable.

So, the first new rule of logic to use, in a market like this, is to pick something that really works, and ignore all the hype, the long lists of features, the fancy software, the big brand names, etc. Someday, the technology will be advanced enough and widespread enough, that everything will be good enough. But today the market is flooded by products that are so bad that they are only usable in very limited applications, even with careful adjustments and tuning. Even from leading companies.

It’s not always logical when it comes to analytics, because detection accuracy in the real world is far more complex and challenging than it seems. Only a few of the most advanced technologies are good enough. And the companies with the strongest market recognition have been some of the worst. It’s a strange problem that you don’t usually see. Fortunately, more and more companies are finding success with video analytics, because they are first finding something that really works, and then learning the best places to use it.

 

 

July 20, 2010

Simplifying Video Coverage

Posted by Doug Marman under Cameras, Megapixel, PTZ cameras, Video analytics
1 Comment 

Technology can make our lives a lot easier and a lot more complex at the same time.

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.

 

May 25, 2010

Getting Our Head Out of the Clouds

Posted by Doug Marman under Edge Storage, Remote Monitoring, Video analytics, Video storage, VideoIQ
[2] Comments 

Cloud computing has generated huge press lately. It is being called the next big transformation in Information Technology. So, naturally, we would expect to see cloud computing being promoted in the physical security industry, especially with video surveillance, which is so compute intensive. But this is where the myths begin. Don’t get caught up in the marketing hype. Cloud computing is not going to be a big benefit to video surveillance.

There are major economic advantages driving cloud computing in the IT world, but those same economies don’t work with video surveillance. This is glossed over and never mentioned in all the articles and ads that tout cloud computing for video surveillance. Companies just want to use the “cloud computing” label to get attention, when, in fact, if anything, video surveillance is moving in the opposite direction – more and more towards intelligence and storage at the edge.

Why doesn’t cloud computing work for the video surveillance market? After all, video compression and storage are some of the most processor intensive functions in physical security, especially if you try to add video analytics processing. But there are big differences, and once you look at the math, it is clear that someone has their head up in the clouds to be suggesting it be used for surveillance.

There are three big cost advantages coming from cloud computing in the IT world:

First, it is more economical with virtualized services. This means that you can save money running your sales management software tools on the same server as another company’s manufacturing resource planning software, or whatever other kinds of software other companies might want to run. Servers have been doing this for years in data centers. Now, they can do it across the Internet for many companies in one location.

Second, you get added savings because you no one uses their software all of the time, it is usually only needed in spurts. So, you reap the gains by sharing servers with others, by maximizing shared processing power.

Third, bringing  lots of server applications under the roof of one specialist company means they can manage everything more efficiently and take the headaches away from lots of individual companies trying to do it themselves.

For the above reasons, cloud computing service providers are saving IT managers 25% or more. That is significant. This assures that cloud computing will grow fast.

But these benefits don’t fly with video surveillance. Here are the problems:

  1. You can’t virtualize NVR storage, because the video is recording continuously. This is the same problem that IP video systems have in the data room. IT managers would like to combine them onto their other servers, but they can’t do it because the video is streaming into the servers continuously. It is being recorded non-stop. In fact, video recording is so processor intensive that traditional servers fail, which is why the industry has introducing new server designs specifically for video surveillance. You can’t virtualize the NVR and run it on another server.
  2. Most software applications see people retrieving data about 50% of the time  and storing data 50% of the time, but those applications are generally idle and waiting most of the time. NVR storage is recording 100% of the time, with huge bandwidth demands, while people only play back the video less than 1% of the time. The reason you can only run about 30 cameras (this varies by resolution and frame rate being recorded) on a good server is because you run out of bandwidth and processing power. So, there are no benefits gained from idle time.
  3. In fact, it is far more expensive to centralize storage because of the above problems and the huge bandwidth needed by the video. This is why most hosted video services are finding that it makes far more sense to put a DVR or NVR at the site with the cameras, or to use cameras with built-in storage, rather than continuously streaming the video back to a central server. It is far less expensive.

As I said above, the real technology shift in video surveillance is in the opposite direction: Intelligent storage at the edge.

This becomes even clearer when you start talking about video analytics. Some companies offering a hosted video service are trying to stream video back to a central server. They are trying to make it sound as if cloud computing is going to save the end user money. In fact, their solution is more expensive, but if they don’t put in a DVR they can save up front costs for the customer. Unfortunately, they seriously cut back the video resolution and frame rate: You aren’t going to get anything close to high quality video this way. They won’t even be able to offer standard resolution, never mind megapixel video to their customers. It is more expensive, on an ongoing basis, but it does eliminate the cost of the DVR.

But when you try to offer video analytics, you can’t play that game: You need high quality, high frame rate video. That makes the so-called cloud computing video surveillance system a non-starter. You can’t get there from here.

This doesn’t mean that Hosted Video and Security as a Service (SaaS) aren’t growing fast. They are, and for good reasons, but none of those reasons have to do with cloud computing economics. They are growing because of superior ease of use for end users, friendly browser interfaces that they can access from any computer, and by providing remote services for end users. It is an ideal solution for small businesses and small retail stores that want to add security video.

The term “cloud computing” has been hijacked and is being treated as the next big thing for security. It is causing a revolution in the IT industry, so why shouldn’t it for surveillance? Well, because it ends up costing more, not less, to use it with video.

 

May 17, 2010

As the PTZ Camera Turns

Posted by Doug Marman under Cameras, Megapixel, PTZ cameras, Remote Monitoring, Video analytics, VideoIQ
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The world of pan, tilt, zoom security cameras has changed. Megapixel cameras and video analytics are shifting the role of PTZs.

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.

 

April 27, 2010

When is Storage not Storage?

Posted by Doug Marman under Cameras, Edge Storage, Video analytics, Video storage, VideoIQ
1 Comment 

The latest buzz growing in the video surveillance world is storage in the camera. But storage, in this case, doesn’t always mean storage.

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.

 

April 2, 2010

True Megapixel Analytics Have Arrived

Posted by Doug Marman under Cameras, Edge Storage, Megapixel, Video analytics, Video storage, VideoIQ
[18] Comments 

We put on the first demonstration of true megapixel analytics in the industry at our ISC West booth last week.

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.

 

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