Online Transaction with Credit Card

How to Improve and Strengthen Online Transaction Security

Less Time Outdoors Means More Time Online

While COVID-19 continues to spread, people continue to spend less time outdoors and are making fewer trips to shops in order to curb transmission. To circumvent outings, many have opted to conduct transactions online from home – things like online banking, mobile banking, and online shopping have all seen increases. There has been a 30-40% annual growth in people’s online transactions since the outbreak, which is significant in its wider implications.

Online Payment Graphic

Public health management awareness has increased due to COVID-19 and in order to reduce the number of outbreaks, banks have actively marketed digital access services such as more feature-rich online and mobile banking options. Multi-factor authentication instruments and systems in financial applications solve time & location restrictions and are also cashless. They can also help us avoid potentially dangerous contact and greatly increase the frequency of public use. However, this pandemic has changed life and our transactional patterns, causing some banks to focus on internet banking while closing some physical branches.

Online Transaction Security

As the public’s demand for online transactions has greatly increased, the importance of internet transaction security mechanisms has become more clear. Traditional verification mechanisms are still mainly based on password input, and are matched via mobile phone text messages or email dynamic password verification. However, the security of these users’ information is still at a very high risk of being cracked and obtained for misappropriation.

While fingerprints have been used as a type of multi-factor authentication, the hardware needed for transportable mass adoption is not available. Fortunately, there is another biometric technology available in our smartphones. Combining the camera and some edge AI software, we can use facial recognition as a secure and reliable second layer of authentication in order to access files, systems, or even make financial transactions.

Facial Recognition - Mobile One Time Password

Facial recognition used as a dynamic and secure form of verification treats each face as a unique access key password. Users can be effectively protected through using this unique biometric (their face) and a deep learning recognition engine. With FR-MOTP from Gorilla Technology, each time you need to access a system or make a transaction, your password is the first line of defense and your face is the second and more unique key to making secure transactions. 

Considering the importance and frequency of the many online transactions we make, it’s apparent that multi-factor authentication should be used whenever possible. When facial recognition multi-factor authentication is used regularly, every transaction is made safe and the overall security level of digital transactions on the internet is improved.

First Aid Tent

New Disaster Shelters Use Edge AI to Manage Pandemics

As we all begin to live with the global aftermath of Covid-19, the challenge is in creating smarter and safer disaster shelters to alleviate the heavy loads put on hospitals when another infectious disease breaks out or when the next natural disaster occurs.

During any given season of the year, countries face the threat of natural disasters – typhoons, tornadoes, hurricanes, flooding, wildfires, earthquakes, and tsunamis.

Pain Points of Traditional Disaster Shelters

As Covid-19 has brought to light, creating and using impromptu shelters for disease control and prevention leaves much to be desired. Here are a few ways that traditional disaster shelters have failed in the wake of Covid-19 and taught us lessons about what we can improve upon:

  • Disaster shelters are typically set up in schools, gyms, public halls or community centers with little or no patient-to-patient or doctor-to-patient separation.
  • They require a large number of front-line staff to manage everything on the ground.
  • Due to the urgency in accommodating large numbers of victims in these places, these makeshift indoor spaces are often overcrowded and utilized as much as possible.

Due to the situations above, an unexpected and innumerable amount of group infections occurred when the first wave of Covid-19 hit. Because of that, however, we can learn how to improve on the idea of how a disaster shelter should be created.

New Disaster Shelter Criteria

In countries like Taiwan and Japan where natural disasters occur frequently, governments have come up with guidelines for new kinds of disaster shelters. For example, the Office of the Cabinet of Japan has formulated the following criteria for the creation of these new disaster shelters: 

  • Prepare more sites in advance for disaster shelters in order to reroute much of the affected population.
  • To avoid gathering in large crowds, it is recommended that affected people seek refuge at the homes of relatives or friends. 
  • All people entering the disaster shelter must be physically examined.
  • Disaster shelters should focus on maintaining good ventilation.
  • Disaster shelters must provide dedicated spaces for people with fevers or other symptoms, and provide a dedicated bathroom to people with abnormal health symptoms if possible.
Field Hospital

In the past and under normal conditions, the government advocated that these items be strictly followed. Yet these key guidelines are quite difficult to maintain in an emergency situation.

Edge AI Solutions in New Disaster Shelters

There are many governments and local authorities in countries that are paying attention to how AI technology can be used in shelters in areas at high risk for natural disasters. For example, using an access control system with contactless temperature detection combined with facial recognition technology can record body temperature and personal data when anyone enters the shelter. That data can then be linked to a specific face identity. Because the system is contactless, it can help avoid transmission between the front-line staff and the admitted person because the device replaces the front-line staff with technology.

Furthermore, physical methods have been used to block people from associating with one another. This is achieved by creating spaces separated by plastic or cardboard dividers. Video analytics can be used to confirm the number of people in each area is within limits. Using AI identification along with personnel management can work together to efficiently strengthen protocols and efficiency.

In addition, mask detection video analytics can be used to identify whether or not an entrant is actually wearing a mask. This cuts the common reliance on staff to check at entry and exit points, thus again reducing the need for front-line staff.

Loss or theft of relief supplies for disaster shelters is often a problem. It’s impossible to allocate people to monitor these materials 24/7. If there are non-staff wandering around the storage area, video analytics can alert the proper personnel and warn them to take necessary action.

Finally, in case an infected person does enter a shelter, focusing on contact tracing methods to effectively locate all the people they have interacted with to prevent further spread of the disease is important as well. Carrying out effective contact tracing, quarantines, and similar measures can benefit from technology and is being closely watched by governments and local administrations.

Covid-19 Alert

Transforming the Traditional with Edge AI

Covid-19 is a pandemic the likes of which the world has never seen and transforming traditional shelters into a more intelligent disaster refuge is an issue that all governments must address. Gorilla’s edge AI technology is leveraged in creating a variety of post-pandemic solutions that governments and countries can adopt. In the future, Gorilla Technology will continue to produce the most advanced video analytics and will continue to develop products that contribute to society.

Video Analytics Technology Main Image

Video Analytics Technology

What is Video Analytics?

Picture this, you’re in a crowded train station and have lost your friend in the mix. How do you and your brain go about picking your friend out of the crowd? Do you go through the same process each time you look for something or does it depend somewhat on what you’re seeking? From a human perspective, looking for stuff seems rather straight forward and although we can describe those processes easily to others, the way we search for and identify things generally differs and depends on what we’re searching for. How one goes about finding a lost friend in a station is different than searching for your keys before going to the office.

Video analytics to find someone in a busy station.

Now imagine how we might go about getting computers to look for things. They would need some kind of input to detect specific objects, recognize and differentiate between those, and then notify us somehow when the requested result is found or not. This process is what we call intelligent video analytics, IVA for short.

This article will go into how different kinds of IVA work and also give some examples along the way.

What Video Analytics Does

The processes involved in getting IVA output from software is similar to how people visually detect and recognize things. The essence of what video analytics does is generally described in three steps.

    • Video analytic software breaks down video signals into frames. This article will not describe this step, but understanding digital video and how it works is an interesting topic and good to know before we break down the next steps.
    • The software then splits the video (frames) into video data and analytic data, then uses algorithms to process the analytic data to output specifically desired functions.
    • And finally, it delivers the result in a predetermined manner.
Working through the process.

Approaches to Video Analytic Processing

Let’s get into the details for number two from the above list as it’s what most people have been talking about recently.

Depending on the goal, video needs to be processed using different methods in order to deliver relevant results. Gorilla has categorized the most widely used types of analytics into five fundamental IVA groups which are described in more detail below.

1. Behavior Analytics

These analytics use algorithms that are designed to look for a specific behavior.

Thinking more deeply, a behavior could be defined as action over time. With that in mind, each Behavior Analytic needs more than one frame from the video to determine if an event or behavior has occurred. So it follows that the algorithms in Behavior Analytics look for changes from frame to frame over time to identify a very specific and predefined event or action. We’ve broken down and classified the Behavior Analytics that are used in our solutions here:

People Counting

People Counting Video Analytics

The People Counting IVA does just that, it detects and counts people for a specified amount of time as they enter a zone and/or cross a line which users define in the software.

Line Crossing

Line Crossing Video Analytics

This IVA detects when people cross a line (or lines) of user defined length and position.

Intrusion Detection

Intrusion Detection Video Analytics

Intrusion Detection monitors user created zones to detect any activity or entries by moving objects (like people).

Direction Detection

Direction Detection Video Analytics

This IVA monitors a user created zone for people moving A) within the zone AND B) in the marked direction. Movements in the opposite direction do not trigger an alert.

Direction Violation Detection

Direction Violation Detection Video Analytics

Same as the direction detection IVA but detects and alerts to movements in the opposite direction. As an example, security checks at airports and other transportation hubs stand to benefit from this type of IVA.

Loitering Detection

Loitering Video Analytics

The Loitering Detection IVA monitors figures or people entering and then remaining in a user created zone for a specified period.

2. People/Face Recognition

People and Face Recognition could easily be sliced into two core groups, but we keep them as one since they are so closely related. As Behavior Analytics need to detect human shapes to perform effectively, People/Face Recognition IVAs are next on our list.

Human Detection

Human Detection Video Analytics

The Human Detection IVA detects human figures within the video. Once detected, features like clothing color, gender, eyewear, masks, and age group can be detected as well.

Face Recognition

Face Recognition Video Analytics

This IVA recognizes and identifies faces. This is used in conjunction with Gorilla’s BAP software and its facial recognition database. While uses for this are myriad (and often in the news), we most often see Face Recognition used for Watch Lists, VIP identification, Attendance Systems, and Black Lists.

3. License Plate Recognition

Some people collect license plates and like them because different places have different plates. However, this variety makes it incredibly difficult for one License Plate Recognition (LPR) IVA to work globally (or even just from state to state). Currently, we generally see this IVA added as a customized feature because adding all the different and beautiful plates into the general release of the software would require too much space.

License Plate Recognition Video Analytics

Having said that, there are currently two approaches to LPR.

      • Parking LPR detects the license plates of parked vehicles in user created zones, vehicles travelling slowly, or vehicles stopped at boom gates.
      • Road Traffic LPR detects the license plates of moving vehicles, or vehicles stopped at a stoplight.

4. Object Recognition

Object Recognition Video Analytics

Replace the Face Recognition IVA with any given object and you’ll get the Object Detection IVA. This is where algorithms are used in training the software to detect and recognize a specific object, like a hot dog. There are a lot of different objects in the world (even more than there are license plate types!), so the training and size requirements add up quickly.

5. Business Intelligence

Dashboards in software showing data about various business activities are a valuable asset in just about any retail or enterprise setting. Using video analytics from within a dashboard to enrich and increase results should be a part in everyone’s toolbox.

While the IVAs in numbers one through four above are widely used for surveillance scenarios, there are a magnitude of business scenarios that can reap the benefits that video analytics offers. To see some great examples, check out how Gorilla is applying these to create intelligent solutions for multiple business markets and industries.

Putting the Video Analytic Idea Together

As you read above, these IVAs all orchestrate various algorithms to achieve and deliver results. Essentially though, IVAs detect for and determine if a defined event or behavior has been found or occurs within a video camera’s field of view and then notifies the designated user of the finding.

In a similar manner, most of us go through varying processes depending on if we’re searching for keys at home or for our friend in a busy station.

Video Analytic Processing Power

Thinking about the entire process, could there be a single solution that can do everything effectively? It seems like an insurmountable amount of tasks: from processing each single frame’s analytic data to displaying it together with the video, into creating a complete video system with an array of user selectable & customizable IVA in a building or any other scenario, all the way to putting multiple systems together that report back to a central control center.

It’s not impossible. To demonstrate this, let’s look at what IVAR™ from Gorilla can do and how it operates.

CPU and GPU Video Analytic Processing

Video analytics as a whole requires a lot of dedicated processing power. We should keep in mind here that before optimization and edge devices with capable CPUs, video analytics was processing both video and analytic data on one machine and required additional GPUs to do most of the work. Technology and the ability to split these two up has advanced to the point that it’s now possible to keep the video data at the edge while pushing the analytic data up the network for quick processing.

One technology, which Gorilla was the first to adopt, is the Intel® distribution of the OpenVINO™ toolkit. Using the OpenVINO™ toolkit to optimize IVAR keeps deployment and upkeep costs low while decreasing operating temperatures by minimizing the need for expensive GPUs.

Delivering and Deploying Video Analytics

Considering the multitude of IVA capabilities and applications in the world today, Gorilla is asked about many things regarding delivering and deploying video analytics and the IVAR platform.

Q: How many video feeds can IVAR handle?

A: IVAR is a highly scalable solution that fits nearly any size system, from a single camera with one IVA to multiple systems with hundreds of cameras running multiple IVAs.

Q: I need a complete VMS with integrated IVA, is IVAR right for my company?

A: From using it as a standalone all-in-one video surveillance solution, to integrating via IVAR’s open API, to adding it to an existing Milestone Xprotect® system, IVAR excels at being versatile in suiting your needs.

IVAR Edge AI Video Analytics Dashboard

Final Thoughts on Video Analytics

The next time you find yourself in a crowded station and need to locate a missing friend (which is hopefully never), think of how a computer attached to a camera might go about doing it. The way that video analytics works is an incredibly interesting and broad topic to cover in one article.  If you made it this far in the article, you should now have a solid understanding of how video analytics operates and how video analytic software solutions like IVAR are driving technology forward.

Further Reading on Video Analytics

To read more about edge AI, click here

For more info on IVAR, click here

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