How Facial Recognition Works and How to Get Started in 2022

August 16, 2022

by Tomas Swinburne with No Comment Technology

What is facial recognition? How does facial recognition work?

We’re here to answer those questions and give you a better understanding of this incredibly innovative technology and how to adopt it or adapt to it for your enterprise’s needs.

What is Facial Recognition?

Facial recognition is a technology that matches human faces with biometric data for identification purposes. It’s an innovative method of confirming a person’s identity using unique biometric data based on up to 80 facial nodal points.

These data points are gathered from images, videos, or real-time footage of people and are generally stored in a secure database for future retrieval.

How Facial Recognition Works

Facial recognition creates a database of facial biometric data. This data is extracted from images or videos in which facial nodes are transformed into digitized data points.

Step 1: Extract Facial Biometric Data

A person’s face is captured in a photo or video using facial recognition technology
Facial recognition software (such as Intelligent Video Analytics Recorder) analyzes biometric data to create a digitized and unique face profile

Capturing or uploading several images creates better facial feature extractions.

Step 2: Store Facial Biometric Data

Facial biometric data is given a numerical value and is stored to compare against other face profiles in a database such as Gorilla Technology’s Biometric Analytics Provider

Step 3: Retrieve Facial Biometric Data for Identification

The facial recognition system connects with the facial data to identify and confirm a face profile
It can take up to 0.5 seconds to identify with up to 30 requests per second

Biometric data can also be used to create employee, VIP, and block lists, ensuring that only authorized people can be approved by the facial recognition system.

Key Features of Facial Recognition Systems

Most facial recognition systems can create and manage face profiles, including easy integrations to third-party systems with APIs.

Depending on the system you adopt, you could have up to 100,000 profiles with demographic data, including gender, age, and more, with an intelligent video analytics management system.

How to Get Started with Facial Recognition

Facial Recognition requires a system that you need to build, manage, maintain, and continuously upgrade. You will need the following key components:

Networked Cameras (to provide an image or video data)
Server (to store and process data)
Facial Recognition software (to analyze data)

Video management software collects videos from security cameras while biometric analytics systems analyze facial biometric data. Having these systems on a network edge will enable your business to perform faster data processing without returning biometric data back to the main server for authentication, saving valuable time.

Basic Facial Recognition System Architecture

Most facial recognition systems follow a similar flow of data and processes:

The Growing Widespread Adoption of Facial Recognition

The COVID-19 pandemic increased the widespread use of facial recognition technology as a contactless form of identification. Facial recognition can also be used with other biometric security measures such as fingerprint, iris, and finger vein pattern recognition for multi-factor authentication.

However, what makes facial recognition stand out from these security measures is that:

It’s a contactless form of identification without touchpoints
It’s more convenient and less intrusive
It only takes, on average, 0.5 seconds to verify a face
It can quickly identify missing, unauthorized, and dangerous people effectively

The technology is already in wide use, and applications exist in:

Smart Cars
Smart Government & Public Services
Smart Devices
Smart City & Public Transport
Smart Education
Smart Retail
Smart Healthcare
Smart Homes
Smart Offices
Smart Waste Management
Smart Ports

Facial recognition technology is a standard in our daily lives. With advances in AI and faster processors, facial recognition is a technology adopted as a new benchmark for biometric authentication.

Businesses, educational institutes, transportation hubs, and governments need effective systems to manage the flow of people.

For businesses, this could be managing employee attendance and preventing unauthorized persons from entering premises. For transportation hubs, the genuine danger of terrorism demands robust identification systems.

Gorilla Technology: A Market-leading Face Management System Solution Provider

Building facial recognition systems in-house is time-consuming, costly, difficult to maintain, and requires a high level of expertise.

That’s where Gorilla Technology’s Biometric Authentication Provider (BAP) and IVAR (Intelligent Video Analytics Recorder) come in.

Our out-of-the-box software and hardware solutions were designed with existing AI-level face recognition capabilities to identify and verify facial biometric data.

Get started and check out our video analytics software and how it can be tailored to your enterprise’s business needs, or contact our team directly to schedule a demo.

Get Started Now

Digital Transformation Trends: The Future of Business in 2022

March 8, 2022

by Jack Maxwell with No Comment Technology

Digital transformation is the implementation of using new technologies to improve existing processes or create entirely new ones. Companies are constantly looking for ways to improve their operations and increase productivity — they want to streamline processes, reduce costs, and automate tasks.

Businesses are now moving from a linear model of production to a networked model where information flows freely between employees, suppliers, customers, partners, and other stakeholders. This means that businesses need to rethink their entire value chain.

What is Digital Transformation?

Digital transformation is about creating an environment where all your people can work together seamlessly with technology. It’s about making sure that any employee has access to the right tools and resources they need to do their job effectively.

It’s also about ensuring that you have a system that enables everyone to collaborate across different departments and locations.

The term digital transformation is often used interchangeably with ‘disruptive innovation’. Disruption refers to the disruption caused by technological advances, while digital transformation refers to the changes required to adapt to these disruptive innovations.

The main goal of digital transformation is to make companies more efficient, effective, agile, and innovative.

How is digital transformation deployed in businesses?

Companies around the world are implementing digital transformation initiatives to help them become more competitive. These initiatives include:

Using data analytics to identify problems and opportunities
Creating better customer experiences
Improving operational efficiency
Reducing costs
Increasing revenue
Increasing brand awareness
Improving product quality

Both large corporations and small-to-medium-sized businesses are implementing these initiatives.

What kinds of transformations will we see over the next five years?

Over the next few years, we expect to see these six major types of digital transformation:

1. Business Process Reengineering

Business process reengineering involves automating existing processes and replacing them with automated solutions. For example, companies could use software that automatically generates invoices based on purchase orders instead of manually processing invoices.

2. Internet of Things (IoT)

Nowadays, IoT allows businesses to collect new data and use it to create better products and services. Using IoT, companies can predict future trends and improve existing ones. IoT also helps businesses to create new products and services.

66 percent of executives say that IoT is a strategic necessity for digitally transforming their operations. The overarching reason to adopt IoT is the opportunity this technology offers businesses to collect new and or more data across all operations, including customer data.

3. Cloud Computing

Cloud computing allows companies to outsource IT services to third parties. Instead of having to build and manage their own infrastructure, companies can rent cloud space from providers who offer reliable, scalable, and cost-effective solutions.

4. Video Analytics

Video analytics is using AI technology to analyze or detect patterns in video footage. Video analytics help companies monitor what’s happening in their facilities. The resulting data can provide real-time insights into how things operate.

Video analytics can also help companies identify potential safety issues before they happen. This could save lives by preventing accidents from occurring in the first place.

5. Cybersecurity Analytics

The internet has become a major hub of information for businesses, providing access to customers, partners, competitors, and suppliers. This means that companies must protect their valuable resources from cyber-attacks.

Cybersecurity analytics help companies detect when an attack is occurring and allow them to respond quickly. This prevents breaches from affecting company assets or causing financial losses.

6. Big Data

Big Data is one of the most significant drivers of digital transformation. Companies use big data to improve customer experience, increase operational efficiency, reduce costs, and gain new insights about their markets.

How should companies approach digital transformation?

Digital transformation is not just about new technologies — it’s about changing the way we think and act. It requires a cultural shift within organizations and across industries. Companies must embrace new ways of working, including flexible deployments, remote teams, and automation. They must also rethink processes and systems to ensure they meet customers’ needs.

Why should businesses adopt digital transformation?

If you’re not already working towards digital transformation, then your company is at risk of falling behind and becoming obsolete. The global market for digital transformation is estimated to grow from USD 521.5 billion in 2021 to USD 1247.5 billion by 2026.

Not implementing digital transformation initiatives allows your competitors to gain an edge by using these new technologies to get faster access to markets and wide-reaching customer bases.

In short, digital transformation is one of the most exciting trends in business today. It’s about creating a new experience for consumers and employees alike, and it’s happening across varied industries the world over.

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Current & Future Digital Transformation Trends

October 27, 2021

by J Burrow with One Comment Technology

Digital transformation has become a necessity for organizations that want to thrive in today’s business world. Such a process means adapting their operations so as to shift to the virtual sphere. By integrating digital innovations into their systems, businesses can ensure their long-term stability throughout the digital age.

Though it was once possible for most businesses to use hybrid-digital or non-digital models, COVID-19 cemented the need for digital transformation. Before COVID-19, only 16% of companies in the world utilized technologies to run their businesses. Following lockdown policies, however, businesses only had two choices: to embrace digital transformation or to risk their survival. That said, if you want your business to survive in the digital era, you would do well to staying up-to-date on workplace trends and the possible changes that could be coming down the pipeline. In this vein, here are the current and future digital transformation trends to keep in mind.

Current Trends in Digital Transformation

The three most current trends in digital transformations primarily come from the need to make businesses accessible to both workers and customers. This accessibility was first made possible by cloud-based infrastructures. These cloud services make it possible for organizations to store, manage, and process huge amounts of data on server networks. As such, business teams can remotely access information, build and tests apps, and communicate with customers without the need for high-maintenance and expensive IT infrastructure. This is why cloud services are considered a core component of digital transformation.

The second trend that’s essential for digital transformation is customer data platforms (CDP). These are software programs that collect and organize customer data from emails, social media, and other touchpoints. From these programs, businesses can gather accurate customer profiles that allow them to make decisions based on real data, which keeps in line with their customer’s needs and preferences. As a result, companies can keep track of consumer trends that are integral to their sustainability—all through digital platforms.

Finally, since data is constantly interchanged on digital platforms, the third trend is to deploy heightened cybersecurity measures because they are essential. Data breaches have become huge threats in the digital landscape due to the acceleration of digital transformation. The FBI reported that there were eleven times more phishing complaints in 2020 as compared to 2016. To respond to the rapid digital transformation, educational institutions are training more professionals through online cybersecurity degrees. These programs ensure that cyber professionals can secure cloud migration, protect company data, and manage internal networks through OT security resources. These include video analytics programs that aid in authentication systems as well as IoT-connected smart devices that make for more robust data protection.

Digital Transformation Trends to Expect in the Next Five Years

Companies can access accurate, real-time data by integrating the Internet of Things (IoT) into their systems. IoT refers to a network of internet-connected devices that enable communication and data transfer from various locations. In line with this, McKinsey & Company estimates that more than 43 billion devices will join the IoT family by 2023. This technology is crucial for digital transformation because it can streamline supply chains, automate processes, and improve customer experiences through increased connectivity.

Companies are also finding new ways to leverage edge AI-powered technologies in their operations. This is a promising innovation that can detect, process, and mimic human activities. Thus, it is predicted that edge AI technologies, like video analytics, will be highly utilized in manufacturing processes to increase productivity levels. These digital technologies can reduce human errors and automate processes through machine learning algorithms. Therefore, while edge AI technologies handle operations that can be automated, human workers can focus on more complex tasks. And as AI and machine learning capabilities continue to progress, businesses will find that more of their operations can be assigned to smart programs.

Digital Transformation is Just the Beginning

Businesses all across the board are investing in technological innovations to make their operations more suitable for the digital age. With the help of technology, businesses can improve their systems, strategies, and customer experience. And following these trends, the business landscape is sure to progress even further, gaining access to more advanced tools such as IoT technology and AI software. Indeed, the industry finds itself in digitally rich times and businesses would benefit to invest early in these technological tools.

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5 Types of Image Annotation Used in Edge AI and Computer Vision

October 20, 2021

by Melanie Johnson with No Comment Technology

The quality of a machine learning model is as good as the data used to train the model. In this context, data labeling is essential to build a top-performing model for your project.

Machine learning models learn by being exposed to training data repeatedly, i.e., image or video annotation data. There are many image annotation techniques, but it doesn’t mean you have to utilize all of them. Learning the intricacies of each and having a general understanding of what every annotation type is used for will help you understand which one best serves your project needs.

In this blog, we will go through how image annotation works in video analytics, the different types of image annotation, and individual use cases. We will also cover the importance of image annotation in edge AI and ML (Machine Learning) to better understand the context of the subject discussed. For now, let us have a bird’s-eye view of some of the image annotation types out there:

Bounding Boxes
Polygonal Annotation
3D Cuboids
Line Annotation
Landmark Annotation

Bounding Boxes

Of all image annotation types used in edge AI and computer vision, bounding boxes are the most common. This type of annotation is versatile and simple in how it encloses and locates objects of interest. With this type of annotation, rectangular boxes are used to detect the location of the object. They are created by simply specifying x and y axis coordinates in the upper-left and lower-right corners of the boxes.

A common application of bounding boxes is in self-driving transport systems. These autonomous driving systems are capable of locating cars on the road. Bounding boxes can also be used in construction sites where drones are used to monitor progress from laying the foundation of a residential building to its completion.

Polygonal Annotation

Unlike bounding boxes which use rectangles, polygonal annotation uses complex polygons to define the object’s shape and location with higher accuracy. Polygonal annotation is preferred more for computer vision projects because it cuts all unnecessary pixels or noise around the object that can impair a model’s accuracy.

Polygonal annotation is also commonly used in autonomous driving, where irregularly shaped objects such as street signs and trees can be precisely located and highlighted, unlike with bounding boxes.

3D Cuboids

3D Cuboids are considered ‘cousins’ to bounding boxes, the only difference being depth, height, and width in object representation. A 3D object representation means computer vision algorithms can perceive volume and orientation, something which 2D bounding boxes cannot interpret. Image annotators using 3D Cuboids simply place and connect anchor points at the edges of an object then fill the spaces between anchors with a line.

In self-driving technology, this annotation type is used to measure the distance of objects from a given vehicle.

Line Annotation

Line annotation uses lines and splines, essentially used to delineate boundaries from one part of an image to another. Computer vision specialists use line annotation in cases where a region that needs annotation is too small or thin to use other annotation types.

A common application of line annotation is for lane recognition and detection with autonomous vehicles. Line annotation is also used in cases such as training warehouse robots to recognize the differences between parts of a conveyor belt. In other words, splines and lines are most effective in situations where important features are linear in appearance.

Landmark Annotation

Landmark annotation is done by creating dots or points in an image and is used to create training data for computer vision projects. With this image annotation type, dots are used to label objects in images with numerous small objects. The size of the dots can vary depending on the landmark areas.

Landmark annotation is commonly used in facial recognition, where many landmarks can be tracked to recognize emotions or any other facial features with ease. Other applications of landmark annotation include aerial views of cities where objects such as trees and cars can be found easily using dot annotation.

How is Image Annotation Important in AI and ML?

Computer vision professionals are seeking to tap into ‘untapped’ fields of AI and ML, improving the performance and efficiency of existing models in the process. That said, ML training data is critical in the improvement of AI’s performance. Below are the three main reasons why image annotation is important for AI and ML:

1. Detecting Objects of Interest in Images

Objects of interest can be detected by machines only through image or video annotation. Machines need to be trained to detect various types of objects in their natural environment. Robots, for example, cannot detect objects of interest unless trained through a particular process.

2. Varied Objects Classification

There are cases where different types of objects are present in an image, and machines are unable to classify them. Image annotation helps machines classify these objects easily.

3. Different Objects Class Recognition

Machines cannot recognize different types of objects in an image unless trained to do so. Object recognition, in such cases, is needed to recognize the objects which appear to have the same dimensions.

Project Success Depends on Proper Image Annotation

Selecting the right image annotation tools is the secret behind every successful computer vision project—be it edge AI, ML, video analytics, or image analytics. The best way to go about it is to choose the type that suits your particular use case or project scenario. Keep in mind that the best data annotation process is the one that guarantees the best quality and accuracy in the final rollout of the model.

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1. Edge AI & Video Analytics – Video Security

Data Bridge Market Research shows us that the rising need and demand for edge computing programs and services will experience a CAGR of 20.15% with a rise of up to USD 2.7 billion through 2028. To refine that further to include video analytics into the edge AI equation, MarketsandMarkets Research show that video analytics on edge devices improves surveillance and monitoring by reducing the amount of raw data that needs to be transmitted over networks.

Processing facial recognition or other intelligent video analytics to identify things of interest and suspicious activities or behaviors on edge devices is a growing and valuable technology in the surveillance and security fields.

EXAMPLE: The IVAR™ edge AI platform from Gorilla Technology

2. OT Security Solutions – Network Security

OT Security is different from standard IT Security. (OT stands for Operational Technology while IT is Information Technology.) OT is a type of hardware and software used to monitor and control physical devices, processes, and infrastructure. OT Security is a common form of cybersecurity used to protect Industrial Systems and networks from various attacks. OT is used across multiple industries including manufacturing, power plants, transportation, utilities, and smart city appliances.

As outlined in a recent report from Market Research Future analysis, the global OT security market is forecast to see an increase to USD 3.5 billion at a CAGR of 42.2% through 2025. The driving forces behind the market growth include the integration of IT and OT systems, surge in the risk of cyber threats on critical infrastructure, and increasing dependence on legacy systems.

EXAMPLE: The NSGuard Series of OT Security Solutions from Gorilla

3. Access Control Systems – Video Security

COVID-19 has changed quite a bit in regards to access control systems and what they are expected to do. Edge AI video analytics are being implemented more in access control systems to accomplish things like employee ID verification via face recognition. From monitoring employee health conditions and meeting government mask/temperature regulations to ensuring visitors and employees can gain timed access with privileges to pre-assigned areas on to time-logging every area entry and exit, the desired tasks assigned to access control systems have expanded quite a bit. While its roots are in security, access control systems are now just as important for health and controlling the spread of deadly diseases.

Mordor Intelligence reports that the Access Control market is expected to reach USD 11.7 billion by 2026 and grow at a CAGR of 7.38% until then. The need to enhance the safety and security across various residential and commercial segments is significantly adding to the market growth globally. The advancements in edge AI computing mentioned above and the expanded use cases in this post-pandemic era have both contributed a great deal to the growing interest in access control systems.

EXAMPLE: Post-Pandemic Access Control & Area Management from Gorilla

4. IoT Sensors – Video Security

The global smart (IoT) sensor market size, according to Allied Market Research, is projected to reach $91.37 Billion by 2027 with a CAGR of 14.30%. IoT sensors enable better control and monitoring of different security operations. These sensors react to physical inputs that occur or change – like light, heat, motion, moisture, or pressure – by producing an output on a display or transmitting data further processing on edge devices or other designated machines with the help of signal conditioning, embedded algorithms, and digital interfaces.

These sensors are often used in the surveillance and security fields along with other technology – specifically enterprise security. The demand for combining video surveillance, edge AI video analytics, and IoT sensors to create comprehensive physical security systems with multiple false-positive checks is increasing rapidly – and so is using OT security solutions to ensure device and health and security.

EXAMPLE: Enterprise Security Solutions from Gorilla

5. VMS Systems – Video Security

Kenneth Research reported to MarketWatch that the global Video Management Software (VMS) industry is anticipated to see a CAGR of more than 16.85% through to 2025. Factors pushing this projected industry growth include easier ways of deployment, the rising use of IP videos, third-party integration, and other digital business systems. Mainly due to COVID-19, safety and security concerns have escalated nearly everywhere and further advancements in edge AI video analytic processing capability have led many VMS users to upgrade their systems to include some form of video analytics as well as OT security.

Observations in Video Security and Network Security Tech

CCTV cameras only sending video to hard drives, guards staring at video walls, ‘systems’ made up of only motion sensors, and unmanaged device/system health – these old days of security are over. As you read in the sections above, video analytics & edge AI, OT security, access control, and integrated IoT is the combined direction that the field is moving. So meet the future today and contact Gorilla about one or more of the ideas in this article.

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1. Lanner – IPC [outdoor/rough environments]

The LEC-2290 from Lanner is the latest generation of intelligent edge computing systems powered by the 8th generation Intel® Core™ i7 processor. Designed for deployment in diverse environments (-20°C to 45°C) and to meet the high Power POE requirements of network applications, the LEC-2290 brings the most secure, low-latency video AI applications, such as city surveillance, machine vision, and mobile surveillance.

2. Gorilla – Micro PC [indoor & building scenarios]

The Behavior Analytics AI Appliance gives government, transportation, enterprise, and retail administrators a better way to monitor, manage, and make decisions on any scale. It has been implemented in many projects globally and excels at superior public safety, enterprise security, and retail analytics. With professional, standard, and custom models to choose from, this AI Appliance runs its preloaded video analytics on up to six video channels – all on Intel® CPUs and without GPU assistance. This is one in a series of AI Appliances from Gorilla aptly dubbed Vision AI. The hardware and software in each AI Appliance in the series is tested for compatibility and stability to give you the optimal combination of accuracy, performance, and efficiency. To go the extra mile, flexible hardware aftercare from service centers all over the world as well as first line tech support from Gorilla is included with each AI Appliance.

3. Arrow – Tower PC [indoor & building environments]

The Edge AI Video Analytics platform from Arrow and Gorilla includes a complete technology stack to develop intelligent video surveillance applications. The platform delivers real-time intelligent video analytics and business intelligence.

Core video analytics capabilities of the solution include people/face recognition, behavior analysis, vehicle detection/recognition, and real-time insights. Exception alerts based on key business or security parameters make the platform well suited for video surveillance applications in public safety, smart cities, and enterprise security and for enhancing customer experiences in retail. Moreover, these features provide a complete security convergence platform that safeguards both physical and network security.

4. Gorilla - All types of AI Appliances and AI Boards are possible through the integration of Gorilla’s AI Toolkit.

Comprised of Gorilla’s edge-computing video analytics software, an AI Toolkit can be placed on hardware devices and boards with different form factors, shortening the time to market for end product manufacturers and solution developers.

More About AI Appliances

Many elements go into making a quality AI Appliance. From form factor, to environmental capability and on to processing power or type of AI being run, the market today is sure to provide the perfect AI Appliance for any setting or project.

You can find out more about AI Appliances and Gorilla Technology here: https://www.gorilla-technology.com/

We also recommend learning more about edge AI here: https://www.gorilla-technology.com/Edge-AI

A firm understanding of video analytics can be ascertained here: https://www.gorilla-technology.com/Video-Analytics

For a deeper dive, download Technical Whitepapers and Case Studies from Gorilla Technology here: https://www.gorilla-technology.com/Edge-AI/Whitepapers

New Disaster Shelters Use Edge AI to Manage Pandemics

November 13, 2020

by Kirin Lee with No Comment Post-Pandemic Topics

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.

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.

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.

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Digital Transformation in the Age of Covid-19

October 26, 2020

by Gorilla Technology with No Comment Post-Pandemic Topics Technology

Only 16% of companies in the world highly depend on digital technologies to run their businesses, while the rest are hybrid-digital and non-digital models.[i] The COVID-19 pandemic has changed all that and has forced companies to finally embrace digital transformation.

Digital transformation is the process whereby businesses adopt new technologies and fundamentally change the way they deliver services to customers. Namely, digital transformation is how enterprises integrate digital technologies into business operations.

Because of the lockdown policies and work-from-home guidelines from Covid-19, many workforces have had to shift their operations to the digital world in order to maintain stability/profitability. This unprecedented and unpredictable crisis has shown the world just how important technologies are to modern businesses. Enterprises now have to rethink their digital transformation strategies and the pace with which to roll them out.

But the barriers for companies to rapidly overhaul their business environments are many. No clear strategies and budget shortfalls are the two biggest challenges most companies face. Yet many businesses are taking the leap since the pandemic leaves them no other options.

Levi’s is an example of a company that has successfully reduced losses by accelerating their digital transformation. They automated most of their logistical processes, deployed an omni-channel e-commerce and delivery system, and invested heavily in data and artificial intelligence, thereby keeping them afloat. By strengthening their connection between digital technologies and operations has been beneficial for both customers and employees.

It’s not just retail—factories, hospitals and governments can also take an advantage of transforming digitally. To embrace a data-driven future, the following technologies are the key trends which we’re already seeing help people adapt to the new normal:

During this challenging time, companies should stay open to digital transformation. Not only the pandemic but also the development of Edge AI/computing are pushing companies to speed up their plan of digital transformation. Gorilla who has specialized in edge computing can really help organizations easily transition and make operations smoother in the long-run.

Click here to learn more about Gorilla’s post-pandemic solutions: https://www.gorilla-technology.com/IVAR/Post-Pandemic-Area-Management

[i] Harvard Business review

https://hbr.org/resources/pdfs/comm/microsoft/Competingin2020.pdf

5 Ways Video Analytics Can Help Manage Covid-19

September 16, 2020

by Mike Wang with No Comment Post-Pandemic Topics Solutions

Since the pandemic began, wearing face masks and social distancing have been used as preventive guidelines for individuals, organizations, and governments to follow. Many governments have gone so far as to make those guidelines into mandates and laws.

Whether guidelines or laws, it’s still the responsibility of society to maintain these and make sure a second wave of the pandemic does not accelerate out of control. But how can organizations or governments monitor or even execute these rules with limited human resources and no smart methodologies on hand?

We recommend intelligent video analytics — AI technology interpreting IP video sources. Preventive measures like mask wearing and social distancing guidelines can be easily monitored as well as executed. Below are five examples of intelligent video analytics from Gorilla Technology and their use cases that can help you make a proactive difference in responding to the Covid-19 pandemic:

1. Facial Recognition

Employing no-touch access control reduces physical contact with devices and people, which can drastically reduce transmission of the virus.

2. Mask/No-Mask & Eyewear Facial Recognition

Mask guidelines and mandates are in effect to some degree all over the world and leveraging edge AI to assist in enforcing these regulations decreases required manpower and increases efficacy in areas where personal protective equipment is required.

3. Crowd Detection with Occupancy Analysis

As social distancing is part of the new normal, people counting and crowd detection can help keep transmission risks to a minimum.

4. Crowd Detection

Once offices reopen, management can ensure break rooms and other gathering spots remain safe and not overcrowded with edge AI zone capacity alerts.

5. Heatmap and People Counting

Good air quality in high foot traffic areas is important and heatmap & people counting analytics can empower HVAC systems to circulate air appropriately to keep infection risks low.

We firmly believe in and see the benefits of implementing intelligent video analytics that can adapt to many situations — it saves time, money, and makes it easier to quickly solve new and unforeseen challenges, like what we are experiencing now with Covid-19.

For more info about this topic, refer to our solutions on Post-Pandemic Area Management. Also, if you’d like a deeper dive on the technology or specific cases, our whitepapers offer more details.

Post-Pandemic Situational Monitoring with Big Data

August 11, 2020

by Mark Tsai with No Comment Post-Pandemic Topics

The period of lockdowns around the world is coming to an end. It is inevitable that business and operations need to reopen for economic survival. However, the possibility of new waves of the virus after re-opening is an issue that requires close monitoring.

Balancing Economic Activities and Epidemic Control

The answer to this problem is actively sought out by various government agencies. From gradual re-openings within borders, to the conditional re-openings at borders (such as travel bubbles), these policies and measures are already starting to be implemented in many areas.

The most important supporting measure after a lockdown – apart from wearing masks, social distancing, and frequent hand-washing – should be epidemic surveillance. This means monitoring those in home quarantines and self-isolation. For example, once a confirmed case is discovered, the history of who they came in contact with needs to be determined in the shortest time possible. Once those potentially infected people are discovered, monitoring them and possibly enforcing home quarantine and self-isolation is a high priority.

When monitoring is not automated, the staffing required will be massive and it may not be possible to execute quickly. A failure like this could cause a large-scale community-based spread of the virus that cannot be traced, which would then lead to the city needing to return to lockdowns yet again.

Considering automated monitoring, it can be applied via the deployment and use of post-pandemic area management solutions like big data analysis. From monitoring home quarantines and whether the monitoring device of a quarantined person strays from its designated location, to understanding the previous movements of a newly diagnosed person – automated big data analysis can be applied to identify possible connections quickly, accurately, and efficiently.

The monitoring devices used for quarantine and self-isolation are IoT devices that can report back to the big data platform at set times. Once an irregular report from the IoT device is recorded, the big data platform can immediately issue an alarm to notify personnel.

Understanding Contact History with Big Data

To accurately understand the contact history of a newly diagnosed person, data from multiple sources is required. For example, the logs from a telecommunication company’s cell tower, check-in/out times from locations using ID verification, CCTV recordings, credit card spending history, etc. would all be utilized in contact tracing. These data can ultimately be analyzed to find possible contacts with location/time crossovers.

For integrating heterogeneous data from multiple sources, Gorilla’s big data analysis platform provides real-time event notifications and space–time analysis functionality, making it an optimal solution for contact tracing.

Contact us to learn more about post-pandemic area management solutions like our big data platform. If you’d like to read more in-depth on the topic, please check out the whitepaper we recently published here: https://www.gorilla-technology.com/Edge-AI/whitepapers