Top Retail Surveillance Strategies

The Top 10 Retail Surveillance Strategies for Success

Retail surveillance is becoming smart.

The hybridization of traditional retail surveillance with AI-based video analytics is fast becoming the norm. Retailers are driving success through measurable and actionable insights delivered by retail analytics.

But if you’re new to all this—where do you start?

A great starting point is understanding how and why retail surveillance is driving innovation through intelligent video analytics (IVA) and the top 10 strategies you can adopt for success.

The Future of Retail Surveillance – Intelligent Video Analytics

Smart retail utilizes IVA to record, analyze, and aggregate real-time customer data and leverages dashboards to deliver actionable insights to craft the best customer experience, which boosts sales.

IVA enables decision-makers to make critical business decisions to improve:

  • Operational efficiency
  • Product placement strategies
  • Store layout optimization
  • Theft detection and prevention

But how exactly is this achieved? Here are ten strategies in smart retail surveillance that give businesses the insights they need to succeed.

The Top 10 Strategies in Smart Retail Surveillance

1. People Counting

People counting tracks the number of customers that enter the store or are within a defined region of the store. 

With this data, managers can analyze how dimensions like advertising, displays, time of day, day of the week, location, etc. influence the frequency of visits.

2. Entrants Vs. Passerby Surveillance

IVA can identify and distinguish the number of shoppers entering your retail outlets compared to foot traffic walking past. 

This gives insights into the amount of foot traffic passing your stores versus how much is entering.

3. Dwell Time Surveillance 

Retailers use dwell time to determine how long a customer stays within a pre-defined store, department, or region.

This data offers insights into what displays and products pique a customer’s interest and how much time they spend deciding on a purchase.

4. Occupancy Time Surveillance

With the occupancy time IVA, retailers can determine how long customers stay in different areas of their stores. In addition, occupancy time data is fed into heatmaps, which visually display store areas with the most foot traffic, with red and orange regions representing higher rates of foot traffic activity.

Businesses use occupancy time and heatmaps to analyze peak and non-peak times to optimize operational efficiency further, which we will discuss later.

5. Intrusion Detection & Prevention

IVA allows retailers to detect unauthorized customers and staff in predefined areas. This aids in detecting unauthorized access to restricted areas, suspicious loiterers, and blocklisted people. 

This can help prevent break-ins and customers or staff from entering sensitive or dangerous parts of your business premises.

6. Merchandise & Aisle Activity

Understanding which areas of a store are visited more than others is paramount for the success of any retailer.

While sales tracking software can offer insights into what customers purchase, it is vital to understand where customers make their purchasing decisions in-store. 

Merchandise and aisle activity offers these insights and allows retailers to analyze what areas of their stores are busier than others and how to prevent bottlenecks of customers with better product placements to improve customer flow and the overall shopping experience. 

7. Age and Gender Demographics

Retailers that don’t understand their customers can’t sell to them, and a key data point for success is demographic market analysis. Smart retailers utilize IVA to track variables such as age and gender to gain insights. 

Dimensionalizing age groups and genders lets smart retailers tune shopping experiences to match demographic product preferences. 

8. Heat Mapping 

Fuelled by people counting, dwell time, occupancy time, merchandise, and aisle activity, customer heat maps give retailers a visual representation of “hot” and “cold” zones for customer activities. 

Analytical heat maps track customer traffic concentration and the resting zone by tracking customer paths and occupancy time.

This data is essential for managers to manage staff and tune customer experiences and advertising efforts.

9. Multi-store Operations Management

Managing multiple stores requires a centralized database to streamline business operations and analytics.

Smart retailers utilize multi-store operations management tools with online dashboards to record, analyze, and report on store traffic management, performance, and popular product zones across stores.

These tools can give retailers real-time data on how stores’ opening and closing hours, weather, time of day, day of the week, time of year, purchase patterns, and demographics affect business success and which stores are their top performers.

10. Smart Retail Business Intelligence Dashboards

While the data collection and analysis process is complex, the ability to consume insights shouldn’t be. Analytical dashboards are critical for displaying actionable insights with supporting data points.

Innovative retailers now use business intelligence dashboards that feed customizable retail data points into one consolidated display.

These insights empower decision-makers to boost store operational efficiency by optimizing customer experiences in near real-time.

Such dashboards help managers to understand how cross-store campaigns perform and make real-time adjustments across the organization for enhanced business outcomes. 

How Does Smart Retail IVA Work?

Smart retail IVA works in four steps to monitor customers, collect and analyze data, store, and report main findings. These steps go as follows:

Step 1: Retail Data Collection

Surveillance and security cameras monitor areas, and the video is fed into IVA software.

Step 2: Retail Data Analysis

IVA performs comprehensive analytics on the footage in real-time to produce data points such as the number of customers, merchandise activity, aisle traffic, dwell time, and age & gender analysis. 

Step 3: Data Management & Storage

Retail data points are then stored securely in a cloud, in an on-premise server, or in a hybrid cloud.

Step 4: Access & Reporting

Retail data points are presented through actionable graphs, charts, and widgets for detailed reporting on a centralized platform for managers to access and assess the necessary changes they need to drive into their business.

The Retailer’s Pathway to Business Intelligence for Success – Gorilla Smart Retail

With many strategies, retailers must adopt IVA to streamline operations and increase margins and profitability. 

Gorilla Smart Retail is a comprehensive, real-time solution for your surveillance system to provide insights into top-performing store layouts, shoppers, and conversion rates by delivering actionable insights for greater business outcomes.

Are you ready to enhance your retail surveillance with IVA for business success?


Facial Recognition

How Facial Recognition Works and How to Get Started in 2022

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:

  1. Networked Cameras (to provide an image or video data)
  2. Server (to store and process data)
  3. 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:

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

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

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.


Digital Transformation Trends: The Future of Business in 2022

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.


Current & Future Digital Transformation Trends

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

Image Showing Digital Transformation Planning.

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

Image Showing Digital Transformation Results.

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.


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

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

Bounding box being drawn over a face.

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

Polygonal annotation being drawn over a boat.

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 cuboid being drawn over a car.

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 being drawn over street lines.

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 being drawn over a face.

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.


Top 5 Video Security & Network Security Tech Solutions Today

What comes to mind when you think about surveillance & security tech? Some companies still only rely on a guard looking at a video wall, strategically placed motion sensors, or badge ID cards for building/office access. Yet many organizations and enterprises are undergoing digital transformations to increase efficiency and upgrade these systems.

In this article we will offer our picks for the top five physical and network security tech solutions that organizations and enterprises are exploring these days.

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

CCTV - Video Security

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

Access Control - Video Security

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

IoT - Video Security

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.

Control Room - Video 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.


Top Video Analytics AI Appliances for 2021

AI Appliances are computers that do one thing well – run specific AI software. Smaller devices with more processing power have come together with developments in edge computing to unlock the immense expansion of capabilities and uses these AI Appliances can now offer.

But what are the devices we think will have the greatest impact in the coming year? As recent reports project, the video analytics market is poised to see large growth and Gorilla is a driving force in that projection.

With that in mind and to keep this list more concise we decided to focus on AI Appliances that run video analytic software since AI running on AI Appliances will vary from project to project. (click on the titles or images below to learn more about each AI Appliance)

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.

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.

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.

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:

We also recommend learning more about edge AI here:

A firm understanding of video analytics can be ascertained here:

For a deeper dive, download Technical Whitepapers and Case Studies from Gorilla Technology here:


Digital Transformation in the Age of Covid-19

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.

Woman wearing mask working from home

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:

icon - video iot

Video IoT

• Video analytics helps to enhance the authentication systems (using facial recognition for touchless entry) • Additional functions are being released, such as mask & temperature detection or crowd density detection to follow Covid-19 regulations.

icon - cybersecurity


• As enterprises embrace digital transformation, their data needs to be saved and shared safely. Cybersecurity solutions help organizations to avoid hackers and to ensure the internal and external networks are well guarded.

icon - big data

Big Data

• By transforming unstructured data into structured information, big data optimizes and accelerates data analysis much better than before. Big data solutions can collate disparate information and allow organizations to make more informed decisions.

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:


[i] Harvard Business review


Minimizing Network Security Risks While Working from Home

WFH is Here - Probably for Good

More companies are transitioning from working at the office to working from home — it’s a fact. What started as a short-term workaround in response to the Covid-19 pandemic, just might turn into a long-term or even permanent change as more workers get used to the freedoms and slashed commute times that working from home affords. 

Employees are not the only ones benefiting from this new normal of more employees working from home, companies are reaping rewards like lower overhead and increased productivity from employees.

Network Security Risks Involved in WFH

In the transition from the office to a home environment , laptops are now a major security risk to companies. Previously, most PCs and laptops rarely left company grounds. So, even if a device was misplaced, the chance of an outsider gaining access to the device, and thereby the entire network, was slim. Employees using laptops out of the office creates a high probability that a non-employee will have physical access to these off-site devices (and in turn, network access). In order to mitigate this security issue, two-factor authorization (2FA) can be used.

2FA with Facial Recognition

With Gorilla’s FR-MOTP (Facial Recognition – Mobile One-Time Password), companies can secure and control employee laptop access by allowing only designated employees to access specific laptops. FR-MOTP works by having the employee use facial recognition on their mobile device to obtain a one-time password in order to unlock their Windows laptop. After installing the app on the device and the software on the laptop, the employee can quickly obtain a password and gain access to their laptop in two steps.

  1. Open the app and use facial recognition to display a temporary, one-time password to use on the company laptop.
  2. Enter the password from the app into the laptop along with the employee’s Windows login.

With facial recognition included in the 2FA process, even if an outsider obtains the Windows login info and the user’s mobile phone, they will be unable to log into the laptop because they won’t have access to the employee’s face.

Post-Pandemic Area Solutions like FR-MOTP deployed by enterprises and other operations can ensure that everyone involved can stay safe while being as productive as possible.

For further reading, our recently published whitepapers are recommended as an insightful starting point. If you’d prefer to talk to someone about solutions like what was outlined above, please contact Gorilla.


Reopening Service & Retail Business Safely in the Post-Pandemic World

Lifting the Lockdown with Edge AI Technologies

Countries are gradually lifting their lockdown policies with the hope that life will go back to what we knew before as normal. However, we are all conscious of the fact that the impact of Covid-19 has redefined our understanding of normalcy forever.

One of the most significant consequences of the pandemic is the acceleration in the adoption of technology to optimize and automate operations. We have seen a steep increase in the demand for edge AI solutions over the past months in preparation for the world after COVID-19. Gorilla Technology has developed a dedicated solution that is not only flexible and scalable, but also market-ready. The solution taps existing products which have been customized to focus on helping businesses achieve the following goals:

  • Monitor Social Distancing
  • Optimize Service Efficiency
  • Automate Operational Processes
  • Improve Overall Store Hygiene

The New Normal for Service and Retail Business

It has become increasingly important during lock-down to monitor social distancing. Inside brick and mortar stores such as supermarkets, governments have restricted the number of people allowed within any given area at any one time.

Entrances and Access Control

Currently, staff has been diverted to manage people flow at the entrance of stores – a process that comes wrought with unavoidable human error and inefficiency. To combat this, when more businesses reopen their doors we can deploy and see edge AI video analytics performing automated people counting at the door, with minimal staff engagement required. Screens at entrances will show the exact number of people currently in the store, as well as the max occupancy allowed. The queue outside the store will be separated by marks on the floor. Once inside the store, automatic alcohol dispensers will be placed at various points for customers to use as they like.

For businesses that don’t need to keep their doors open during business hours, an access control device can be deployed at the entrance to measure peoples’ temperatures and grant or deny access accordingly. These devices can be equipped with Gorilla’s facial recognition technology in IVAR™, which includes mask detection and can integrate with temperature sensors. In order to provide flexibility, solutions need to be scalable, cost-efficient, and contactless. The choice will naturally be to deploy edge devices able to run AI analytics and convert this into real-time data. The key tool behind it will be computer vision.

Analytics On the Sales Floor

Inside the store, behavior analytics based on video footage will be used to monitor trends over time. The importance of gaining a bird’s eye view of customer’s behavior in the store is crucial for further action.

Businesses want to understand which areas in the store were frequented more in order to do proper sanitation. This data can also reveal customer preferences, which in turn can lead to more effective inventory management as well as an improvement in in-store product placement. Popular products can be placed in more accessible areas, but also, this can benefit the business owners by allowing them to better manage the shelf-space fee from vendors.

Having precise information about how much time people are spending in certain areas (dwell time) could prove valuable in assisting health organizations perform contact tracing. The contact history of the infected person can then be accurately traced, to inform people that have unknowingly been in contact with them. In addition to this, with the same dwell time information, retailers can correlate the data to uncover the exact product that people are spending more time looking at and tweak their supply chain accordingly.

Previous to the pandemic, most of the sourcing in retail chains was done through personal relationships between vendors and buyers. Decision making was largely made by buyers who relied on their own experience, a system that is time consuming and not quickly replicated. The pressing need to automate and make decisions based on precise data will become indispensable for businesses reopening in the post-pandemic era.

POS and Check-Out

The need for social distancing will not subside for a long time. Some businesses estimate that such measures will need to be kept in place for the next two to three years. In view of this, the areas where people are likely to concentrate within brick and mortar stores are crucial. The check-out area is one of these high risk areas where edge AI video analytics can help manage the amount of people queuing up in order to reduce wait times.

Crowd detection analytics in computer vision solutions like IVAR can help detect the number of people in a determined zone and send a notification to appropriate staff when the amount of people exceeds a pre-established limit. Once staff is notified, they can reduce check-out time by opening more counters to serve more people.

Reopening Through Digital Transformation

If COVID-19 has taught us anything, it is to pause and reflect on our business practices. This forced halt in our daily activities has no doubt impacted us negatively, but has also allowed the world to reset itself and find new best practices.

It has made us humble about our own limitations and brought us together to collaborate as a community. It has put into question the way we consume, and striped our purchases to the bare necessities. It has also pushed organizations that were hesitant about digitalization to quickly pivot and adapt. In the wake of the pandemic, the service and retail industries will go back to business transformed – empowered by AI-driven technology and enhanced by data-driven decision making.

Contact us for more on digital transformations.