There is a lot of buzz in the news lately relating to Artificial Intelligence, Machine Learning, Video Analytics and Facial Recognition. The airline industry is just one area example of this. In fact, the Orlando Airport announced last summer that facial recognition technology will be used on all international travelers.
Travelers from Boston’s Logan International Airport to the Caribbean islands on Jet Blue started a trial program to use Facial Recognition software that acts as a boarding pass. This is the first trial between an airline and Customs and Border Protection. Other airports are also running pilot programs.
Recently, I heard about an Artificial Intelligent Robot that was taught to take on and beat the best poker players in the world. The first attempts, the robot lost. After the robot spent a year learning, it was able to beat the best players in the world.
We are still in the very early stages of AI and machine learning. For example, the chess playing robot has learned how to win the game of chess. If another occurrence happened like a fire breaking out, the robot would not know what to do. A human would be able to process the information about the fire and determine they should get out. The Robot would still be playing chess.
What I am trying to say is as humans, we have the ability to take on so much more information and process that information based on a lifetime of learning. Machine learning, or AI is still in it’s infancy by comparison.
HPE is utilizing Machine Learning with HPE InfoSight to improve customer support. Infosight has been supported with the Nimble and 3PAR storage platforms and recently added support for the Proliant Servers. Look for additional platforms to be supported in the future.
Using HPE Infosight, HPE has been able to prevent downtime on the Nimble storage platform using the InfoSight Predictive Analytics. HPE recently published a white paper titled HPE Nimble Storage Sets Six Nines Availability Standard. This standard of system availability is a result of using the predictive analytics along with machine learning.
InfoSight collects and correlates data from every storage array implemented across their install base, enabling global visibility and learning. This allows InfoSight to predict potential issues before they happen, before they cause outages for customers. This in turn provides a better support experience for the customer.
This capability will also be available for the Proliant, BladeSystem, Synergy and Apollo systems. InfoSight for Servers will proactively collect and analyze each servers Active Health System data to identify configuration, health and performance information, then recommend solutions. It will also provide warranty and support status. All this information is consolidated and provided through a global dashboard.
Machine learning can also be utilized in Video analytics and facial recognition. As stated previously, the airline industry is looking at using facial recognition software to replace the boarding pass.
There are also several other use cases. Facial recognition is beginning to be implemented in law enforcement, corrections facilities, stadiums, schools, casinos, along with the airports and other transportation industries. Where this goes over the next 2 years is going to be very interesting.
Artificial Intelligence and Video analytics can be used in several ways. Here are just a few:
The use cases are limitless as people look at what can be done and has this technology become more mature.
Several things we have noticed that make video analytics a reality is better quality cameras, the ability to do real time alerting, the integration with mobile devices, scalability of the software, accuracy of recognition and above all, the privacy of the individual.
Hold on, this is going to be interesting.
Imagine a manufacturing company with an 80,000 square foot plant, multiple exits and shipping docks, 200 employees and several products which are as small as the palm of a hand or as big as the flatbed of a pickup truck.
Obviously a lot of elements to this scenario. Now imagine there has been theft which has been taking place during and after hours of operation. This threat has been going on for at least six months and product loss has started to affect cost of goods.
This is where video analytics comes into play. Now, there might (actually should) be video cameras set up throughout the facility, but they are missing the video analytics component. With video analytics, the possibilities of detection are endless and evolving every day.
The specific package for this scenario could be “Anomaly Detection.” Anomaly detection has the ability to pick up on something that is not deemed normal in any environment. For example, if all employees are wearing uniforms, but there’s one individual who is not wearing a uniform and is hanging around an exit door or shipping document, this would send an alert for security to investigate.
Or another example would be if running wasn’t allowed in the facility, and cameras show two or three individuals (wearing uniforms) running towards an exit door. Is there a fire, emergency or are they actually trying to steal something? Another possibility is there is supposed to be 7 employees in a designated area and there are only 6 or more than 7, anomaly detection has the ability to send out an alert to specific personnel to investigate the abnormal activity.
Another package for this scenario could be “Facial Recognition.” Maybe the facility doesn’t have uniforms for its employees, but through the use of Facial recognition, the organization knows exactly who’s working for the company and where their work-station is. Each morning or evening when employees walk, facial recognition could identify all the certified employees. However, what if one person wasn’t identified. Who is he/she? Where is he/she? What is he/she doing? Again, another alert could be sent to Security to investigate.
Maybe there’s a specific section of the plant where the majority of theft is taking place. Facial Recognition could identify the employees who have that work station and Anomaly detection might be able to identify abnormal activity from a specific employee or employees.
The options and scenarios are endless and over time, video analytics has the ability to self-learn. By Machine Learning, the ability to identify abnormal activities will continue to multiply and also identify exactly who is supposed to be (and not supposed to be) where and when.
This is just one example where video analytics could be useful in so many ways; safety, cost-efficiency, theft prevention, security, etc.
A casino would be able to identify when a ‘high-roller’ walks into their establishment. The concierge would greet that person with the ‘VIP’ treatment (i.e. favorite beverage, favorite table, etc.). It would also be able to identify abnormalities which could spot card-counters or cheating in anyway. Underage gambling may also be reduced due to facial recognition. Addicted gamblers could be removed from the building if they put themselves on a self-ban list.
An issue at the forefront right now and probably the most important issue at hand is children’s safety in schools. Anomaly detection would be able to detect if a person should or should not be in a specific location of the school. Facial recognition could identify whether the person is a student, employee or security guard. If not, alerts can be executed and initiate all sorts of preventative measures (classroom doors auto-lock, 911 notified, security guards sent to the specific area, etc.).
What about school buses? Facial recognition would be able to identify and count the number of children on a specific bus. If John or Jane Smith is not on the bus, an alert would be notified. Anomaly detection could identify bullying or fighting or any abnormal behavior taking place on the bus. What if a random person tried to board the bus?
Again, the options are endless. There will be a lot of learning from a human standpoint and a machine learning standpoint. As listed here, I think we all would agree there are certain areas which need to be implemented as soon as possible.
Most companies are gathering trillions of bytes of data, day after day, at no small cost, and then doing very little with it. Worse still, the data often is not serving its primary function very cost effectively. The “culprit,” so to speak, is video surveillance data, the information captured by the video cameras that are used throughout most modern facilities. But the situation is changing rapidly, thanks to the new landscape of Video AI Analytics Applications.
Video Analytics can be described as “the emerging technology where computer vision is used to filter and manage real time CCTV video for security and intelligent traffic monitoring.”
Simply put, Video Analytics is an automated approach to managing and analyzing video, without the cost or man-hours previously required. There are many different brands and technology platforms for Video Analytics, but they all work on the same basic principles, using pattern recognition and other Algorithms technology to provide two critical capabilities: Recognize unusual activities as they happen and notify the security system in real-time.
Today’s Video Analytics software offers growing functionality. For example, it can be programmed to look for specifically defined anomalies. It can even be programmed to give special attention to specific elements in a video frame—such as a computer, door, or filing cabinet. Video Analytics software tracks people and objects, and can send alarms when suspicious activities occur. Furthermore, Video Analytics can be integrated with other security and information systems to create new possibilities for using and managing video data.
The first step for most companies is to use Video Analytics to support and enhance guard performance. In this application, Video Analytics software continuously monitors everything that happens in the field of vision of every surveillance camera, every second of every day. When it sees suspicious or unusual patterns and activities, it sends an alert to the security system so guards can look at the monitors, see first-hand what is happening, and take any needed immediate action. It can also trigger other security events, such as coordinating the motion of several cameras to track the movement of a suspect through a facility. In some programs this is called “guard service.” Guard service should make every guard more effective, so companies find they can improve security and reduce staff at the same time. Recent studies show that it’s common for guard service applications to generate savings of 75% or more in total guard costs.
Adding on to that value, Video Analytics can then be linked to card access systems to improve security. A card being used in an unauthorized or suspicious way can trigger cameras to zero in on the event and record the time and other information in a searchable video log. The real power of Video Analytics may be the fact that it turns analog video into useable data that can be analyzed, searched and managed. This opens endless possibilities for guiding decisions on facility use, energy consumption, personnel safety, and many other issues. Vitally is the improved response time. You can simply find information and act on it more quickly with Video Analytics, whether the problem is a break-in happening right now, or a building use problem that pops up every day.
After collecting and storing petabytes of video data, organizations now want more value out of their investment. Video Analytics provides an answer, helping most companies improve security and lower costs. By starting with applications that deliver rapid ROI, like guard enhancement, companies can implement the technology in a way that pays for itself. The value can then be extended to other security and information systems, leveraging many technology investments to improve security and building management.
I am the father of two amazing kids (a girl & boy) and I am now the grandfather of a grandson & a granddaughter. Each time I hear about a school shooting, my heart goes out to the parents, grandparents, brothers, sisters, and the entire community of those injured or killed. Each time, my wife and I talk about the number of lives that are forever crushed by such a senseless act.
I also have the honor of leading an IT Organization (Zunesis, Inc.) and an exceptional group of people who make it their business to help make the lives of people better through the application of information technology. For many years, Zunesis has been providing IT solutions to K12 School Districts in the Western United States. Video surveillance is used widely within K12 schools and is a useful technology in understanding what has happened after the fact. For many school shootings, video surveillance has been used as a tool to document what happened when, where and by whom. These benefits of video surveillance are useful, but they are 100% reactive. What if we could use a combination of video and Internet of Things (IoT) technologies to attack the problem of school shootings proactively?
Rapid advancement and innovation in IoT and video analytics may provide a preventative tool to help protect our kids from further senseless shootings. In a nutshell, the idea is to use higher fidelity 4K video cameras and co-resident video analytics software to monitor school activities, entrances, and exits. Advances in IoT allow for computer intelligence to be put at the edge of the network and provide an early warning system, alarm system, or possibly a system that triggers automated functions based on what is seen (like locking a door and preventing a would-be perpetrator from entering the school).
In the past, we have used video to go back and examine what has already happened. Often it is viewed in a central location by someone watching a multitude of screens while likely being asked to do other jobs as well. This new strategy moves the video and intelligence away from a central viewing area and a reactive method, to a proactive, intelligent, at-the-edge approach. Being able to recognize a person or object that doesn’t belong beforehand could provide additional protections for our kids. Ultimately, we want to keep the bad guy(s) out of our schools before they cause harm. In situations where the shooters are students of the school, the video analytics would need to be trained to recognize things that were “out of place” such as large coats (used to conceal weapons) during warm months or students entering the school at unusual times.
High-resolution 4K video cameras and video analytics software allow these IoT systems to perform facial recognition or recognize things that are out of place (like a person wearing a trench coat during a warm month of the year). Would it be possible to load up a data repository with graphical pictures of teachers, administrators, students and parents who attend and work at the school? Would it then be possible to implement specific boundaries around allowable entrances and the times those entrances could be used? The key would be to define which people are “safe” and when they are allowed access to the school. The next step would be to define what is normal and what is abnormal for the purpose of triggering alarms and notifications. Over time, machine learning and artificial intelligence could be utilized help monitoring normal behaviors and reacting to behaviors that are inconsistent.
The next big question is cost? Access to money is always a challenge for our schools. The good news is that 4K video cameras, IoT edge devices, analytics software, and compute and storage solutions (at the edge) are coming down in price. And because video surveillance is being used widely within our school systems already, these new innovations could be used to upgrade the current video systems. It is likely that these new IoT video analytics solutions will be more expensive than the current reactive video surveillance solutions, their cost should not be outside the realm of possibility. Finally, because the K12 market is so large and this need so acute, the technology would surely become more affordable as competition entered the picture and parents, teachers, and politicians became part of the funding discussions.
I recognize that this may sound like a stretch to some, but finding new ways to combat this threat should be considered and IoT and video analytics provide a possible path for that to happen. I for one am encouraged by these new innovative technologies. I believe in the very near future, we will be able to use technology to proactively protect our kids.