March 24, 2009

The Liability of Unmonitored Cameras

Posted by Doug Marman under Cameras, Remote Monitoring, VideoIQ | Tags: , , |
[16] Comments 

Over the last year, we met with dozens of Chief Security Officers from some of the largest companies in the US. We wanted to hear their opinions about our new iCVR camera, which includes video analytics and a built-in DVR.

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

 

March 13, 2009

It’s All About Accuracy

Posted by Doug Marman under VideoIQ | Tags: , |
[2] Comments 

Video analytics performance is all about accuracy of detection.

Any technology requiring calibration or tuning has an accuracy problem. I’m surprised this isn’t mentioned more often.

Why do you need to tell a system what the size of a human or a vehicle is before it will work accurately? The reason is simple: The technology can’t distinguish humans and vehicles from other false alarms accurately enough.

A bird doesn’t look much like a person. A tree branch doesn’t either. Humans don’t have a problem telling the difference. But this is what calibration is doing – it is trying to eliminate these kinds of false detections.

The only reason that VideoIQ’s technology needs no calibration or tuning to work reliably in even the toughest outdoor environments is because it is accurate enough. The difference in accuracy is astounding compared to all others. It’s not just a little bit better. You won’t often find such a big discrepancy in a field with so many players.

Even the best of the best analytics products require size calibration to work reliably. Comparing uncalibrated systems against the VideoIQ iCVR shows how dramatically better the accuracy is. Even the best systems aren’t close.

When it comes to comparing advanced motion detection systems (http://spotonsecurity.com/2009/02/06/the-big-video-analytics-lie/), that sometimes try to pass themselves off as video analytics, the contrast is even more dramatic. They are unusable outdoors without calibration. That’s how bad their detection accuracy is.

Eliminating the need for calibration and tuning is not just about ease of set-up. That’s a huge benefit, but accuracy becomes an issue everywhere: Where you can use it, who can install it, how many problems will it generate.

For example, over a year ago, one of the largest video analytics companies advertised partnering with a company who wanted to use their technology for protecting a particular residential and commercial application. It was supposed to be a nationwide roll-out. There was one limitation: The system needed to be installed by untrained technicians and therefore calibration could not be used. So, they limited the range of detection, turned down the sensitivity and tried to detect only static scenes, mostly areas of cement – not where other moving objects might be present, such as bushes or trees, etc. In other words, just looking at an area of cement with everything else masked off.

Even those limitations weren’t enough. Shadows from nearby trees would often fall into the detection areas. Reflections from the sun or headlights would cross into the regions of interest. Leaves, paper or dead branches would cause false alarms. After some extensive testing, it became obvious the technology simply wouldn’t work.

The question isn’t how well does the technology work when installed and continually re-tuned by a trained professional, but how well it performs when anyone uses it.

It all comes down to detection accuracy.

 

February 27, 2009

The Megapixel Lens Problem

Posted by Doug Marman under VideoIQ
[17] Comments 

Image sensor technology is outpacing lens designs, which is leading us into a strange world.

You can now buy 5 megapixel surveillance cameras from a number of vendors. However, there are no 5 megapixel lenses!

Sound strange? Unfortunately, it seems to be true. If anyone knows of a real 5 MP lens for surveillance, I’d love to hear about it.

We’ve talked to all the major lens manufacturers. They all advertise megapixel lenses, but they don’t say how many megapixels their lenses are designed for. When you push them for an answer, you find out that none have 5 MP lenses.

We even asked the lens manufacturers who make the lenses that the megapixel camera vendors recommend for their 5 MP cameras. They told us that their lenses were good for 1 MP or sometimes up to 2 MP. We haven’t found a 5 MP lens yet, except for the fisheye lens from Theia.

This means that you aren’t going to get the full value of all those extra megapixels. You might still be able to cover a much wider area than a single lower resolution camera, but it won’t have all the detail you think it will.

Our camera expert at VideoIQ, Steve Lefkowitz, who has been working with lens vendors for over 20 years, asked them a good question:  If they can design lenses for the 8 MP and 10 MP point and shoot consumer digital cameras, why can’t they make one for surveillance?

Apparently, the situation is no better for consumer cameras: The imagers may be capable of 8 MP or 10 MP, but the lenses fall far short. They don’t even come close.

This is bizarre.

Here’s a little extra info on lenses. When evaluating the quality of a lens, there are three main specs to look at:

  1. Resolving power or resolution: This means how many horizontal lines the lens can accurately distinguish. But you need to be careful that you find out the resolution not just in the middle of the lens, but check the edges and corners as well, since they generally have much better resolution in the center. A good lens is sharp to the edges.
  2. Geometry or distortion, which means how round a circle will look anywhere in the scene. A circle will often look perfect in the middle, but looks like an oval in the corners. This kind of distortion is common. A smart digital camera can actually correct distortion like this, but as far as I know the only MP company doing this today is Mobotix.
  3. Flatness of field, which means that when the lens is in focus at its center, it is also in focus in the corners. Often this isn’t true. No, it isn’t your eyes, it’s the lens.

Another little know fact that our expert Steve told me: Everyone looks at the F-Stop spec, but this only shows the amount of light coming through the iris. The real spec to know is the T-Stop, which tells you the amount of light coming through the iris and all of the lens elements. A good 1.4 F-Stop lens might have a T-Stop of 1.5, which means that the lens elements aren’t blocking much of the light. But a poor 1.4 F-Stop lens might have a T-Stop of 1.8.

If you want more info on megapixel lenses, check out John Honovich’s recent column:

http://ipvideomarket.info/report/the_importance_of_megapixel_cameras

I think it would be a big help if the lens manufacturers started being more up front about their full lens specs. I can’t imagine why the good lens manufacturers wouldn’t lead this change, since the specs will show how much better their lenses are. Why not show why it is worth paying more for good quality?

Unforunately, dealers are left to figuring this out by trial and error. Dealers come to learn the hard way that some lenses are a lot better than others, but why put the burden on them to figure this out? Hiding the specs doesn’t help anyone.

We’ve seen the IP camera manufacturers coming together to establish standards. Why not the lens manufacturers?

 

February 13, 2009

Big Brother versus Little Brother

Posted by Doug Marman under Cameras, Remote Monitoring, VideoIQ | Tags: , |
[2] Comments 

A bit of philosophy in this post. Sometimes it helps to step back and look at the whole picture.

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.

 

February 6, 2009

The Gap in Perception

Posted by Doug Marman under VideoIQ
1 Comment 

I ran across the following article from John Honovich at his IPVideoMarket.info web site, which is chock full of valuable information about video surveillance:

http://ipvideomarket.info/report/do_video_analytics_work

I highly recommend John’s site.

In this article he raised his concern about the gap in perception between manufacturers of video analytics equipment and the users. Integrators who have tried using analytics products often say it doesn’t work, while all the manufacturers that John talked to said their technology does.

How can this be?

John writes:

Manufacturers generally have a significantly lower standard for determining what works than customers or integrators.  This is not an accident yet it is generally not an issue of malice.  Most manufactures, especially at the senior management level, possess little domain knowledge, resulting in routine underestimation of the needs of their customers.

This is certainly true, and it is a big factor. Many video analytics companies have come from out of University labs or from Computer Vision developers. They saw the security industry as a great place for their technology, but had no real experience in what security professionals have to face in the real world.

As John points out there is also a gap between what a new technology is capable of. When people see a breakthrough, the imagination can immediately jump to Star Trek tricorders and dylithium crystals.

However, I think there is another very big factor at play in this case. It has to do with calibration and tuning.

The best of the Video Analytics companies have tried to simplify this process and make it as straightforward and repeatable as possible. Only VideoIQ has eliminated the need for calibration and tuning, while providing accurate detection. As a result, we’ve seen a much smaller gap from our customers. Our end users are generally surprised at how well the technology detects. They, of course, hope we continue to improve, but rather than being disappointed they seem to be pleasantly surprised.

Here is what I think is happening: Manufacturers are familiar with their technology and their products, so they have learned how to tune and calibrate their systems to make them work well. However, integrators and installers, even after training, will never know how to program their product as well as the engineer who designed it.

I was talking to an engineer, at our VideoIQ booth at a trade show, who ran tests at Cisco when they were evaluating the leading video analytics technologies. He no longer works there now, but he told me about one of the products they thought was the most promising. He said it worked great.

I asked him why they hadn’t adopted the products into the Cisco line, if they worked so well. He said:

“When the head engineer installed the products, they just plain worked. It was remarkable. He knew exactly how to set it up. But, even after our engineers were trained how to calibrate and tune the system, we could never get it to work reliably. Only the head engineer could make it work.”

In other words, a big part of this perception gap comes because manufacturers know how well their products act when installed properly. That’s their perception and that’s what they mean when they say it works. But integrators see only how well the systems work when they try to install them.

What matters is not how accurate and reliable a technology is when a trained specialist installs it – but how well it performs when any video installer puts it in.

That’s an important gap to keep in mind.

 

February 6, 2009

The Big Video Analytics Lie

Posted by Doug Marman under Cameras, VideoIQ
[16] Comments 

With any major technology shift, there are often discrepancies between what the public imagines and what is practical.

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.

 

February 6, 2009

San Francisco Surveillance Study Shows Need for Analytics and Real Time Response

Posted by Doug Marman under VideoIQ
[4] Comments 

San Francisco recently released one of the most detailed reports on municipal surveillance:

http://www.sfgate.com/cgi-bin/article.cgi?f=/c/a/2009/01/11/MNLU156OAF.DTL

It points out serious weaknesses in most systems that are easily correctable. Video analytics provides the answer to many of the problems they experienced.

Perhaps the most significant statement from the report is:

“We find no evidence of an impact of (the cameras) on violent crime,” the report stated. “Violent incidents do not decline in areas near the cameras relative to areas further away (and) we observe no decline in violent crimes occurring in public places.” (Quoted from the San Francisco Chronicle article above.)

This was not true for other crimes. For example, larceny targeting people, vehicles and homes dropped 24 percent near cameras. The report surmises that violent crimes did not see the same reduction because they are “often committed outside the bounds of rationality.” In other words, knowing that cameras are present does not deter these acts of passion.

But this misses the real problem. The following comment from the Chronicle points to the biggest weakness of the San Francisco system:

In addition, the cameras have not had a “feedback cycle” – criminals have not seen immediate consequences to lawbreaking under the cameras’ gaze.

San Francisco has a particular restriction that you won’t see in many cities: They are not allowed to monitor live video. They can only retrieve recorded video after a specific incident. This prevents any ability to stop crime through response. Thus, video becomes nothing more than a deterrent, and is used for gathering evidence after the offense.

This is like having police officers standing by watching an event unfold, but not being allowed to help until after the murder or theft takes place. The purpose of this restriction is to protect privacy. This reveals one of the biggest issues that will need to be addressed in the coming years, and I’ll explore in a separate blog post:

Most crime happens under the cover of privacy. Therefore, there is an inherent conflict between privacy concerns and the ability to reduce crime.

The next revealing part of the San Francisco study jumps out of this quote from the Chronicle:

“Officers and others note that despite poor image quality, (the camera) footage has been useful in criminal investigations … more often footage is helpful in establishing a sequence of events for a crime or placing witnesses at a scene,” researchers wrote.

Poor image quality. This was one of the big criticisms. But there were other serious shortfalls:

  • No dedicated manager for the program
  • No training of the officers for how to find or view the recorded video
  • A lack of storage space produced video that was lacking clarity
  • Recording only retained one week’s worth of video, when a full month was needed

The cameras used in San Francisco recorded only 3-4 frames per second due to storage limitations. They paid $700,000 to install high resolution cameras, but the video was so choppy that they often could not read license plates and missed important details of the crime. And with only a week’s worth of storage, there was often no video to retrieve when they needed it.

Their cost estimate to increase the storage for faster frame rates and one month’s worth of recording: $3M, which they can’t afford at this time. That’s over four times the cost of the cameras installed.

This sums up the problems with most video surveillance systems:

  1. They only record: No active monitoring and no immediate response
  2. They typically store video at CIF resolution (352 X 240 pixels) and 3-5 frames per second
  3. They often don’t retain storage long enough

Why hamstring the system this way? Especially after you pay good money to install high quality cameras?

The reasons are simple. It is too expensive to actively monitor video using security officers to watch monitors. And the cost for bandwidth and storage for full motion video at full D1 resolution (704 X 480 pixels) for a month is too high. This is why most systems are compromised.

Video analytics offers an answer to all of these problems.

It is easy for video analytics to watch all the cameras all of the time. They can automatically alert a security officer if a breach is occurring, or warn when a crowd is developing and a potential problem might be brewing. With audio through the cameras, you can respond immediately to prevent crimes before they happen.

The report raised this very observation, saying that it would help to use “smart cameras that are capable of sounding an alarm if a gun is brandished, a fence is jumped, or a person falls down.” (It isn’t easy for analytics systems to detect guns, but fence jumping and trip-and-fall detection are common.)

As for the cost of storage, the problem becomes obvious. The cost of storing 5 frames per second (fps) at CIF resolution for a month, using MPEG-4 video, costs an end-user about $100 per camera. Raising this to 15 fps at D1 resolution increases the end user’s cost and bandwidth by 9X. If you jump from 1 week’s worth of storage to a month, you’ve increased it by 4X again.

With analytics, you can significantly reduce these storage costs by using what we at VideoIQ call Content Aware Storage. That simply means that the analytics can see when something important is happening and the system then records high quality, full motion video. When nothing is happening it records at traditional low quality, low frame rates.

Video analytics is not just for detection and pro-active response. It can make the whole video system smarter and more effective. And it solves the biggest issues that are compromising the effectiveness of surveillance systems today.

 

February 6, 2009

The 10,000 foot view

Posted by Doug Marman under Cameras, VideoIQ
Leave a Comment 

Since the mid-1990’s, when a variety of technologies began converging, it became clear that digital video was going to change the face of security:

  • 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.

 

« Previous Page

Follow

Get every new post delivered to your Inbox.