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Top 10 Computer Vision trends to look for in 2022

Thanks to the pandemic, businesses have witnessed transformation breakthroughs during the last two years that were to happen in the next 5 years. The technology adoption trend will continue to accelerate. It mainly consists of AI and intelligent industrial automation. Although businesses are still in the process of getting a firm grip over various AI technologies, computer vision will continue to open various technology horizons for hyper-digital dawn. Video intelligence- one of the most talked computer vision Technologies holds tons of value in the real world. According to Forbes, the computer vision market will reach USD 49 billion by 2022.

Computer vision sees, understands and analyzes the objects in images and videos

What is Computer Vision, and how does it function?

“Computer Vision enables computers to see, observe and understand where AI enables them to think and act accordingly.”- Wobot.ai

Computer vision is the ability of any computer to see and understand the world and make decisions based on their visual insights and rational understanding. In simple words, it automates and augments human sight in computers.

Video analytics or video intelligence powered by computer vision- received by categorizing and identifying the objects in the video with the help of bounding boxes are revolutionizing businesses and empowering them to mitigate risk, automate surveillance, superfine security and help them achieve operational efficiency.

The advances in computer vision dominated the year 2021. Let’s explore how it is going to perform in the upcoming year and the top 10 trends that are considered to lead the tech race during 2022.

1. Garner safety in public, private and workplaces

Challenge: Businesses are slowly gaining momentum after the huge halt received by COVID-19. It is immensely important to ensure a safe organizational culture to withstand such a crisis in future. The US federal agency OSHA (Occupational Safety and Health Administration) mandates employers to protect employees from workplace hazards that can cause any illness or injury. Organizations are facing hard times to adhere to this newly-defined and ever-changing safety and hygiene protocol and require a robust automated solution to ensure employees’ hygiene and safety.

Computer Vision for safety: Computer vision being the key business enabler, provides vision or video intelligence that is being utilized by many businesses worldwide to implement safety measures. Best-of-breed AI with Health, Safety and Environment (HSE) video event detectors supercharge cameras to automate monitoring and analysis. The video intelligence finds the events like the absence of a hardhat, safety vest or face mask or anything that causes the safety protocol breaches.

Undoubtedly it will strengthen the HSE initiatives in 2022.

Play Video(Explore how Wobot.ai’s video intelligence enables to have a proactive approach to regulatory safety breaches and get notifications of raised tickets)

2. Elevating quality analysis operations

Challenge: In a ruthlessly competitive market, quality failure is not an option. Countless production, packing and dispatch procedures must ensure that faults and abnormalities are continuously monitored. Manual monitoring is not well-equipped to rule out errors or faults, leading to substandard quality of products or services. It can cause irreparable damage to the brand image. With labor shortage and lack of technology expertise, many businesses are struggling to stay in the race.

Computer Vision for quality inspection: Computer vision can be a savior for businesses like food and beverage, automotive, manufacturing, retail etc., that have been observing shrinking labor and declining margins. Computer vision optimizes visual inspection to achieve better quality, accuracy, and flexibility at a very marginal cost.

Play Video

3. Leveraging Edge Computing

Challenge: Businesses are highly prone to network failures. Lacking technology expertise, most of them fall flat to quickly process and analyze the humongous data they come across daily.

Computer Vision for Edge Computing: The most-talked topic of computer vision- Edge Computing- a distributed computing framework that brings enterprise applications closer to data sources such as IoT devices or local edge servers. It enables businesses to process and analyze a large amount of data more quickly at the site of collection. This leads to gathering actionable real-time analysis, insights and calculations. Software deployed on Edge computing automates and accelerates the cycle time and monitoring of labor-intensive processes. It quickly connects to cameras and Video Management Systems (VMS) to detect and prevent safety breaches in real-time.

4. Gathering and annotating data

Challenge: Many organizations use a manual workforce for data annotation. Manual annotation makes it tedious and complicated. It also brings in the probability of human error and hampers annotation quality and speed with increased cost.

Computer Vision for data/image/video annotation: The advancements in computer vision helps in automating the data labelling and future-proofing their training workflows. Various training workflows used by businesses today will seamlessly fuel data pipelines for faster activation of computer vision applications. It will accelerate data processing with minimum or no errors. 2022 will be the revolutionary year as we will witness an end-to-end automated annotation for images and videos.

5. Supercharging cameras for thermal image analysis

Challenge: The existing CCTV infrastructures in most businesses follow the 2D inspection and lack smartness. They are unable to provide visual insights like wait times, presence of metal or weapon detectors, absence of safety kit, hard hat, facial mask, etc. Lack of video intelligence and limited visible spectrum necessitate human or manual intervention to detect events.

Computer Vision for Non-destructive Testing (NDT): Computer vision-powered thermal cameras have night vision tools for surveillance and are augmented non-destructive testing. It helps to find events using radiology images taken through NDT techniques.

6. Leveraging SaaS video analytics solution

Challenge: The bottleneck of integrating video analytics solutions across businesses is the cost of implementation. The additional hardware and upgrade in the existing conventional surveillance system cost a bomb.

Computer Vision for Video Analytics on the go: With an explosive demand for video analytics solutions in 2022, businesses would be seeking robust software that is being trained using 300+ different parameters for highly sensitive tracking with minimum hardware or infrastructure upgradation expenses. Computer vision smartens up the video analytics solutions to get robust video insights with minimum or no falls alarms. This software is customizable, scalable and helps to save time and cost.

Play Video

7. Implementing Computer Vision-based closed-loop solutions

Challenge: In a closed-loop system, the performing action depends on the system generated output. Implementing a closed-loop system on computer vision-based systems is challenging because of model accuracy and reliability issues.

Computer vision for the implementation of the closed-loop system: With everything is going autonomous, computer vision-based closed-loop systems or solutions like facial recognition are in huge demand. In the upcoming year, computer vision-based control loop solutions will be largely used in autonomous cars and unmanned vehicles. When conventional sources like accurate position/orientation data (e.g. GPS/IMU) are not available, computer vision uses visual feedback.

In 2022, multiple industrial use cases will be discovered and worked upon to implement closed-loop vision solutions in order to save time and increase production efficiency. It will autonomously control and adhere to the process parameters without interacting with the operator.

8. Implementing a proactive approach for event prevention

Challenge: AI-powered vision analytics solutions detect events and send alerts. However, they don’t have a sophisticated workflow of functioning to analyze the event in-depth to prevent it from repeating.

Computer Vision to understand the reasoning: Computer vision, when combined with the Machine Learning model, provides the reasoning behind the event or predictions without subjecting them to human interpretation. In 2022, computer vision will be combined with many other robust technologies, tools, or frameworks to unveil the reasoning behind the behavior and performance of the computer vision model.

9. Adopting 3D inspection

Challenge: Existing 2D inspection has its limitations and doesn’t provide video insights. It lacks a multi-layered security system because of which events may go unnoticed without any notifications.

Computer Vision for advanced perimeter monitoring: Computer vision-enabled 3D inspection provides automated surveillance insights through sensor data, video feeds and drone imagery. Based on a multi-layered security system, 3D inspection smartly detects intrusion, unidentified object, vehicle and user access control with accuracy and high speed of examination.

10. Bringing efficiency and transparency in Banking

Challenge: Although on a heel of digital disruption and implementation of advanced technology tools, banking is still falling prey to cybercrime and theft. Additionally, analyzing and processing monstrous and sensitive data are laborious activities that cease bank functions to outperform other domains.

Computer Vision for fast-paced data analysis and security: Optical Character Recognition (OCR) technology, a subset of computer vision, captures and extracts humongous financial or banking data quickly and efficiently from various documents or folders. In 2022, various computer vision technologies will be extensively used to perform mile-to-pay facial scanning, micro-expression analysis with virtual-loan officers, conversational bots for simple servicing requests, and humanoid robots. Biometric authentication and facial recognition with other Machine Learning tools will be used to prevent or detect cybercrime or fraud, scan and process documents and protect real-time transactions.

Conclusion

According to Gartner, the global computer vision market size was valued at USD 11.32 billion in 2020 and is expected to expand at a compound annual growth rate (CAGR) of 7.3% from 2021 to 2028.

Undoubtedly in the hyper-digital Post-COVID industrial world, video intelligence is playing a key business enabler as government and other institutions are stressing vision-based inspection system implementation to prevent the crisis, ensure workers’ safety and leverage a hygiene-based economy. This clearly states that computer vision is here to stay and will keep ruling throughout the year in 2022.

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The Role of Artificial Intelligence in Reducing Workplace Stress

Workplace Stress is one of the least talked-about topics when it comes to leading a healthy lifestyle and ensuring mental health. There is enormous scope for educating employees regarding managing their stress and not letting it affect productivity.

As technology is evolving rapidly and artificial intelligence is replacing most manual operations, AI was supposed to replace human jobs resulting in added pressure on employees. However, the statistics tell a different story.

Almost     64% of employees think that automation technology helps them reduce both     workload and stress

70%     of employees are in favor of artificial intelligence replacing manual and     laborious tasks

69%     of employees believe that technology will enhance their jobs and not     replace them.

Surprised? We, definitely, were!

We dig deeper into the role of artificial intelligence in stress management and how it can help reduce workplace stress. This blog will shed some light on some of the fantastic benefits of artificial intelligence to help employees cut down on workplace stress.

Automating Monotonous Tasks

It has been observed that one of the significant areas where employees feel incredibly stressed is doing repetitive tasks. Automating repetitive tasks such as finishing and sending daily reports, performing customer support activities, doing repetitive activities of speaking to customers and delivering the same message repeatedly, etc., allows employees to focus on creative tasks and use their skillset more efficiently.

Many companies implement chatbots for their primary customer support activities. In a survey, it was found that these chatbots are becoming efficient in answering up to 80% of the queries.

This is huge when it comes to allowing your employees to focus on what matters the most and the strength of AI help them do exactly that.

Enabling Stress Sensors and Emotion Recognition

The benefits of technology that’s built to help human problems are huge. The new and developed technology allows understanding the state of mind of a person through their behavior and communication.

This groundbreaking innovation helps understand the emotional quotient of employees. Various AI-powered wearable tools, CCTV cameras developed with deep learning techniques analyze employee behavior and identify employees who are having troubles maintaining their mental health.

Recently, a group of students at MIT designed a wristwatch to track stress levels through their skin. This innovative gadget can tell the mindset of employees by measuring electric changes happening in the nervous system caused due to the sudden changes in the state of mind and get reflected through skin temperature changes.

Using these techniques, companies can encourage employees to take mental health as priority and take measures accordingly. This may include taking periodic breaks based on the emotional state and adopt healthy work practices.

AI in Surrounding Environment

The other important aspect of artificial intelligence is that it can be molded into any shape and size due to its flexibility. AI in IoT is the best example of it.

Using the same technology, it is possible today to implement AI in your workplace surroundings to understand your living habits and making informed decisions using the data gathered.

Implementing AI into surroundings such as emotion recognition sensors, measures that can help increase productivity assist employees cut down stress and be more creative, resulting in better performance.

Managing mental health also depends a lot on the situation of your overall surroundings. Implementing lights that changes their brightness according to day or night time can help employees focus more on work with stable mindset.

Along with executing eco-friendly measures, combining it with artificial intelligence truly transforms the work environment and increases workplace productivity like never before.

Mental Health-friendly Automated Technologies

The future of mental health must be dependent on the growing and ever-evolving technology. For example, various services nowadays use natural language processing (NLP) to create chatbots that can talk with humans using personalized responses. This is highly useful when it comes to performing Cognitive Behavioral Therapy (CBT).

Employees can talk to these chatbots while they are having a low-intensity day and feel refreshed by getting some warmth.

Companies can implement these technologies to help employees offer services to reduce stress in case of increased workload. Therapeutic chatbots help understand your employees’ moods and encourage them to think positively by conversing with affection.

The above examples and use cases of artificial intelligence suggest that AI has a massive role in reducing workplace stress. Employee burnout is something employees face regularly. Implementing solutions that can help employees overcome burnout encourage employees to work in a healthy environment.

Companies must understand the importance of employee mental health and provide solutions that can help them overcome it. Statistically 72% of employees have said that they feel happy when they have a low-stress environment telling the importance of a healthy workplace.

As technology evolves and artificial intelligence reaches newer heights, the benefits of AI in mental health are just a trickle effect. However, adopting AI-powered tools to ensure positive mental health is the future and companies need to implement them sooner than later.

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4 ways Drive-thru can improve the Speed of Service

In the fast-casual and quick-service restaurant industry, improving the speed of service is a never-ending goal. This goal should, however, get all the emphasis it can get. This is because the service speed in a food service establishment, especially a QSR, plays a critical role in driving customer experience and primarily impacts whether customers would like to visit again or not.

As drive-thru sales are increasing manifold during the current times and the vehicle queues get longer, the service speed has suffered. Moreover, the ongoing labor shortage makes it difficult to keep the service time under control.

In fact, as per a study by SeeLevel HX average total time in the drive-thru increased from 327 seconds in 2019 to 356.8 seconds in 2020. This 9.11% increase in time leads to a potential loss of $32,091.33 yearly per store unit.

So, if speed is a critical aspect of customer satisfaction, what can you do to improve it amid an increase in demand?

Let us find out.

Increasing Speed of Service while Improving on Service Quality

The top ways to ensure speed in operations without compromising quality are forecasting traffic, maintaining staffing requirements, training the staff, and ensuring order accuracy. Let us go through each of them in detail.

Forecast Traffic

Proper Staffing

Train Staff

Ensure Order Accuracy

By understanding data, you can make informed decisions about critical factors like peak hours, staffing requirements, and customer wait time. In addition, this data is the key to improving customer experience as this will help ensure that you have enough inventory and staff to fulfill customer orders.

Accurate forecasting does not need to be manual. Video Analytics software can help you streamline everyday operations by providing actionable insights, which can help reduce cost and optimize the speed of service. In addition, for multi-store locations, consolidating this data can help identify problems quickly.

For example, with the help of Automatic Number Plate Recognition (ANPR), you can identify repeat customers, personalize digital menus, and speed up sales.

Or, with vehicle wait-time detection, you can understand how many cars are currently waiting in their lanes and for how long. Based on this, you can study past data to understand improvement areas, analyze strategies, and deploy more staff if needed.

Drive-thrus faces five significant challenges almost daily. Learn about how they can overcome these challenges in our blog on Top 5 Challenges in Drive-thrus.

You can understand staffing requirements and boost sales by studying data trends and peak hours. With the help of these insights, you can schedule enough staff for different shifts and ensure that you are providing customers with optimal service and minimizing labor costs at the same time.

In addition, when you make changes to the schedules in advance, you set your staff up for success.

Training staff properly is the steppingstone that will make your quick service restaurants’ speed of service better than your competitors’. Employees in your drive-thru should be well versed with the menu and the different operational aspects involved.

When employees are present at the cash counter, greet customers, initiate the ordering process, repeat the order, and give the correct add-ons, they automatically decrease the order time.

However, keep in mind that you should give your customers enough time to place the order no matter how much everything is about speed. A customer has a good experience when they are not rushed in their ordering process.

Order mistakes can slow down your entire work pipeline. Therefore, for a faster experience, employees should make sure that the orders are correct. Inaccurate orders are a cause of frustration for the employee and lead to slow service. Employees can repeat the order to the customers once to make sure that everything is correct. In this way, they minimize the chances of error, and it also helps reduce food waste.

When you combine and implement these points in your everyday operations, you give your customers exactly what matters: speed, value, quality, service, and accuracy.

Invest in AI-powered Technology for Ongoing Improvements

When we pair the current labor crisis with the increase in demand for drive-thrus, we experience a crisis-like situation. But there are still so many opportunities for restaurant owners to take up. Various technologies, be in front-of-house or back-of-house, can help you take increasing demand and shortage of staff heads on.

One such technology is video analytics. Wobot’s AI-powered video analytics help drive-thrus optimize customer flow from order taking to exit. With its drive-thru, personal hygiene, fire protection, billing, and covid-19 checklists, Wobot.ai empowers foodservice businesses with actionable insights using their existing CCTV cameras.

Drive-throughs that deploy Wobot.ai can reduce wait-time, manage queue length, track customer drive-offs, get alerts on returning customers, and use data to make critical business decisions.

Discover how you can incorporate Wobot.ai in your businesses https://wobot.ai/drive-thrus-and-qsrs/.

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How will Computer Vision improve the Future of Drive-thrus?

Before the pandemic, the very definition of restaurants was starting to change, thanks to ghost kitchens and virtual brands. When the pandemic struck, and dining got closed due to the Covid-19 outbreak, restauranters realized they don’t need a brick-and-mortar food space to serve food to their customers. Slowly, many restaurants converted their parking spaces to drive-thrus to add to the ongoing demand for them.

In just a few days and months, drive-thru started seeing excessive demand and accounted for 82.4% of the total restaurant sales in 2020.

However, now that labor shortage is plaguing the foodservice industry, we are looking at an unprecedented situation. Labour shortage coupled with increased demand has made food service establishments look for new ways to streamline processes and improve productivity.

This situation enhances the need for AI and automation for scaling processes and increasing operational efficiencies. Computer vision is one such technology. Here, automated AI models collect accurate and reliable data from cameras. The insights generated from this data can help you, as food service establishment owners, to make critical business decisions.

In this blog, we will dive deeper into how computer vision can transform drive-thrus with its applications.

How can Computer Vision Technology help Drive-thrus?

Computer Vision provides real-time data and critical insights on operations so that you can measure, analyze, and act upon crucial data points that can optimize processes and improve labor efficiencies.

Cameras can detect license plates, car models, and vehicle types in your drive-thrus. When you pair this data with POS, voice bots, and other third-party tools (whether predictors), you get the information to identify repeat customers, personalize digital menus, and speed of service. This combined information can also help you upsell by recommending items based on a customer’s past orders, current weather, time of day, and much more.

Top Computer Vision Applications for Drive-thrus

With the help of computer vision, you can see many things with your existing cameras. This section will look at some of the most efficient applications that can transform how your business works.

Queue Length

Vehicle Wait-time

Vehicle Drop-offs

Safety and Cleanliness Measures

Queues cause frustration and delay the pleasure of eating a meal. On average, there are 3.2 vehicles in line when a car pulls into a drive-thru. Even though eliminating queues is not possible, we can reduce queues to manageable sizes with queue length detection.

With queue length detection, you can know how many cars are currently in queues in your various location. Knowing this information can help you split the lines, take orders before a vehicle reaches the order window, and give customers an accurate timeline of how long they might need to wait in the lines. This way, you can manage your customers’ expectations while also reducing their wait time.

When you get real-time visibility into your multiple locations and understand how many vehicles are waiting in the lanes and for how long, you can tackle various challenges that you face every day. One of the biggest challenges here is understanding the difference between expected service time and the actual service time, which can help you optimize your operations.

With wait-time insights provided by computer vision technology, understanding how factors like particular day, time, weather, holidays, and seasons impact your wait-time becomes easier. When you have such insights, you can strategize staffing and inform in-line customers about the approximate serving time ahead of time.

Cameras in your drive-thrus can see the events that you might miss. For example, a lot of the time customers enter your premises and leave without taking an order because of the long queues. You do not even know about their presence, but these drop-offs cost you revenue.

However, Computer Vision powered cameras can see the make, model, type, and license plate in your premises based on your state laws and regulations. Therefore, it can see how many vehicles are pulling out of your establishment without placing an order. By this, you can gain valuable insights into what factors directly affect customer drop-off in your establishment and how much revenue you are losing. You can then create mechanisms to tackle this issue and maximize your sales, profits, and customer satisfaction.

Customers are and will be very anxious about their health and safety in the coming times. It is essential to make Personal Protective Equipment (PPE) mandatory to ease their worry and protect them and your employees from life-threatening viruses. Computer Vision technologies can see PPE, social distancing as well as various cleaning methods in your kitchens. It can see if your staff is wearing facemasks at all times and for how long and they are washing their hands.

If you have a preset cleaning schedule, for example: mopping the floors at 6:00 AM and 6:00 PM, you don’t need to have employees physically present to check the activity, as Computer Vision powered cameras can do it for you.

This way, you can improve back-of-house sanitary measures and protect your employees’ and customers’ wellbeing in real-time by reminding them to follow the safety measures.

The Future of Drive-thrus

The future of drive-thrus is a place with multiple lanes, multiple pickup points, and where technology powers everything. To survive amongst the thriving competition, demand, and labor shortage, you need to take charge of all the different elements that will help you increase service speed while improving the quality of service. For this, it is crucial to consider all the stages of a customer’s journey inside your premises to understand which areas can be improved upon and modified.

Wobot.ai brings various benefits to life for drive-thrus around the world. Wobot’s AI-powered video analytics help you optimize customer flow from order taking to exit. Drive-throughs that deploy Wobot.ai can reduce wait-time, manage queue length, track customer drive-offs, get alerts on returning customers, and use data to make critical business decisions.

Let’s make your food business a success. Visit to know more https://wobot.ai/drive-thrus-and-qsrs/.

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Top 5 Challenges that Drive-thrus Face

Drive-thrus, also known as drive-throughs, have been with us for long, from as early as the 1970s. Since then, their owners have worked hard to make them fast and efficient while trying their best to give the customers a good experience.

They have been consistently going through innovations to enhance employees’ productivity and customers’ experience. Then the pandemic struck. Its orders surged as people became wary of entering restaurants. According to a study by the NPD group, by December 2020, drive-thru lanes accounted for 44% of off-premise orders. However, with this increase in demand came many challenges. The added responsibility of keeping up with the orders on time became all the more prominent.

In this blog, we will dive deeper into the challenges drive-thrus face due to the increase in orders and how they can tackle it.

Top Challenges faced by Drive-thrus

1. Longer Wait-times

Last March, when Covid took over, restaurant sales fell sharply as in-person dining got banned. However, after several weeks, a new trend emerged. There were long lines wrapped around fast-food locations’ entire perimeter. In addition, more and more restaurants began converting their dine-in facilities to drive-throughs by taking advantage of their parking spaces. As a result, higher volume and larger orders slowed down average wait times by nearly 30 seconds in 2020, according to SeeLevel HX’s 2020 study. While the average total time in 2019 was 327 seconds, it increased to 356.8 seconds in 2020, which leads to an approximate $32,091.33 potential loss per year per store unit. While longer lines may seem good for sales, it can result in losses if the drive-thrus are not optimized for increasing demand.

2. Inaccurate Orders

Inaccurate orders can tarnish how your customers feel about your food service establishment. Even if the wait time is less and the customers receive their orders immediately, a wrong order can directly result in a bad customer experience. To rectify this, the customers would have to wait in the lines again to get the correct order, which is a big hassle. Therefore, most of the customers go back deeply dissatisfied with an incorrect order.

                        According to the 2020 SeeLevel HX study, as quick-service chains become more reliant on drive-throughs, it is all the more critical for fast-food brands to improve accuracy to stay competitive. While the service time of accurate orders is 231.9 seconds, it is 281.0 seconds for inaccurate orders, which is approximately 13% of the total orders. As much as this is a customer service issue, it is equally bad for the restaurants as wrong or missing orders will slow down their speed of service as they look to correct the mistakes.

3. Hygiene and Safety in Drive-thrus

Customers are becoming more and more aware of their surroundings. As a result, they now prefer to interact in a place where they can see that hygiene and cleanliness are priorities. These include certain covid-19 specific guidelines such as social distancing, facemasks, and general protection like gloves, hairnets, etc.

According to QSR magazine, there are four aspects of keeping a foodservice establishment clean and sanitary, these include:

§  Handwashing

§  Cleaning surfaces

§  Sanitizing food contact surfaces

§  Disinfecting touch-points

However, keeping at par with these guidelines is challenging. There is a lack of a set mechanism to check if all hygiene, cleanliness, and safety processes are in place and if employees are following them continuously.

4. Identifying Returning Customers

Fast-food chains do not have set ways of recognizing returning customers. This leads to a lack of personalization as customers need to convey their personal information, preferred orders, payment, and billing details again and again. Due to this, order time increases as employees can’t identify repeat customers. In addition, it also takes away the chances of personalizing digital menus, upselling, and speeding up sales for the restaurants.

5. Lack of data-driven Insights

Solving all these issues drive-thrus face daily isn’t a simple thing. It is not about adding more employees or increasing shift timings. Instead, it is about understanding the root cause of the issues with the help of data and working on these data-driven insights to better the operations.

If data is the solution, then why is it difficult for them to work on these issues?

§   

§  They lack data that will help them make an objective decision that will bring good results.

§  Lack of real-time data works against them in making critical decisions at the right time.

§  This lack of real-time data cannot produce actionable insights to help managers and operators take immediate corrective actions.

What Drive-thrus need to Improve?

As the demand for drive-thrus grows, so does the competition. For them to thrive amongst such competition, technology, such as video analytics, is essential to reduce costs, gather actionable insights, and take immediate decisions. Video analytics helps QSRs in increasing operational efficiency, increase sales, thereby driving more revenue.

Wobot.ai is a video analytics platform that helps drive-thrus stand out from the competition. Our AI-powered checklists help QSRs increase efficiency and decrease operational costs. With Wobot.ai, businesses can gain continuous feedback on processes, focus on areas of improvement, and drive operational excellence.

Drive-throughs that deploy Wobot.ai in their premises can reduce wait-time, manage queues efficiently, improve guest experience, and use data to make critical business decisions.

Discover how you can incorporate Wobot.ai for your foodservice establishment 

https://wobot.ai/drive-thrus-and-qsrs/

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Video Analytics: What Does it Mean and How Does it Actually Work?

Over the past few years, video analytics has gained interest from various industries and businesses worldwide. Video analytics, also known as video content analytics, helps automate tasks that were previously entirely dependent on humans. As a result, it leaves a lot of room for businesses to employ their workforce in other crucial jobs, which helps improve productivity and the overall operations of a business. Along with this, video analytics can also support organizations in keeping track of their hygiene, safety, and security.

We now understand what video analytics can do, but how does it actually work and benefit your businesses?

In this blog, you will discover the basic concepts of video analytics, how it works, and how it is used in the real world.

What is video analytics?

Video analytics refers to using Artificial Intelligence to analyze real-time videos to detect anomalies according to pre-fed data. This technology detects and tracks objects, activities, and people and helps in improving day-to-day operations. In addition, it analyses historical and real-time footage to learn from mistakes and applies it to develop solutions and make essential decisions beyond human capabilities.

How does video analytics work?

You might have got an idea of what video analytics means. But how does it work and produce desired results?

1.    Feeding the system

There is a saying, “You are what you consume.” It’s perfectly accurate in the case of AI. The quality of the decision made by Artificial Intelligence is as good as the data it is fed. No matter how advanced the model is, the decision would be substandard if its data is not good. So, feeding the system with the right and extensive historical data will help the AI be in its prime while making important decisions. It is necessary to provide a considerable amount of real-time images, videos and recorded footage to the video analytics software to accurately analyze a video and come up with a decision.

Relevant data comes mainly from CCTV cameras. First, there must have a clear view of the entire territory from different angles. This step enables the software to capture the same visual event from a different perspective so the analysis could be accurate. Gathering more data is good if the system can process it efficiently.

2.    Cloud Computing vs. Edge Computing

In a world where data is precious than oil, a large volume of data is captured every passing second. Hence, it needs to be processed for its analysis to happen. There are two modern technologies for this process:

o   Cloud Computing

Cloud computing is the availability of computer system resources remotely and on-demand without direct active management by a user. As the name suggests, enormous amounts of data are stored in servers, in a cloud or virtual space, instead of hard disks or proprietary local disks. This data can be accessed remotely from anywhere in the world through the internet. Furthermore, once you connect to the web, you can access large amounts of data without being present nearby the database. This lets you access your required data on demand from the comfort of your home.

Cloud computing technology aims to make users capable of using cloud storage without deep knowledge about them. It aims at cost-cutting and lets users focus on core business instead of hindrance by IT drudgery. It mainly works on virtualization technology, separating a computing device into different virtual devices to efficiently manage and perform complex tasks. Virtualization enables the users to speed up their IT operations efficiently and at a low cost.

o   Edge Computing It’s a paradigm involving a distributed network of computers whose components are located on different computer networks which operate on the same communication protocols by passing messages to one another. It brings data storage and computation closer to the work area to improve response time, latency, and bandwidth. Its main applications lie in “instant data” or real-time data processing where all work is outside the cloud.

Edge computing aims to move computation to the edge of networks, far from data centers, utilizing smart objects and network gateways to provide better services and perform tasks efficiently on behalf of the cloud. By moving computation to the edge, it’s easier to dispense content caching, persistent data storage, and better IoT management, which results in better transfer rates and response times.

Video analytics software can either run on cloud servers known as central processing or implanted in cameras themselves, called edge processing. While both processes are good, a cloud solution is preferable for processing real-time camera feeds and complex analytic functionalities for non-critical tasks. In addition, in cloud-based video analytics, there is less upfront investment on hardware, is easy to deploy, and has zero infrastructure cost.

Furthermore, using cloud technology, we can now configure the software to send only actionable data to the servers to reduce network traffic and more storage requirements.

3.    Defining scenarios and training models

Once your physical architecture is set up, we must define the relevant scenarios we want our software to focus on and then train our models to detect and track target events. Let’s take an example of a manufacturing company and how the hardhat, which is commonly used on the site, is recognized with the help of video intelligence.

o   Image Classification

In image classification, the technology identifies what are easily recognizable images or objects using unique colors, pattern, and format. In our example, hardhat can be easily recognized while monitoring operations. This process is known as image classification in layman’s terms.

o   Localization

Now let’s take an example of hardhat placing along with the safety jacket of the same color. Now there are multiple objects and the technology could find challenges to identify it. That’s where localization comes to rescue. It trains the camera to differentiate between multiple objects and provides correct results.

o   Object Detection

However, to attempt localization there needs to be some training involved. That’s where object detection is helpful. It trains the algorithm in a way that it can differentiate between multiple objects and helps us give the right results by identifying key differentiators.

We also need to train our models from scratch, which requires a tremendous amount of effort. But we have some resources available which make this a less tedious task. For example, image datasets such as ImageNet or Microsoft Common Objects in Context (COCO) play a crucial role while training new models. Recently, open-source projects are being published which deal with building a custom video analysis system.

4.    Human Review

Finally, a human is needed to review all the alerts sent by the video analytics software and act upon them. With the help of such advanced systems, operators can now detect main events which may be overlooked or would take several hours to see manually.

Conclusion

Many sectors like manufacturing, retail, food services, hospitality, drive-thrus, and QSRs can benefit from this technology. Let us learn how.

1.    QSRs and Drive-thrus: Drive-thrus can use video analytics to count vehicles, study the wait-time of the vehicles, and also for automatic number plate recognition (ANPR) based on customer identification.

2.    Hospitality: As guest experience is the driving force behind the hospitality industry, video analytics can help assure guests have the best experience by ensuring concierge availability, clean surroundings, and secure premises.

3.    Food Services: Restaurants can benefit significantly from AI-powered video analytics by automating the monitoring of various hygiene, cleanliness, and safety practices such as PPE usage, mopping, handwashing, and many more.

4.    Retail: Video analytics can help retailers understand the traffic areas in their store, manage queue length and footfall.

5.    Manufacturing: From use cases ranging from accident safety to safety gear to assembly line productivity, manufacturers can use intelligent video analytics to improve workplace safety and productivity.

With intelligent video analytics, we can perform tasks more effectively and less tediously, which is also less expensive. Organizations can leverage it to automate tedious and monotonous processes, gain valuable insights and make better business decisions.

About wobot.ai

Wobot.ai is a Video Analytics platform equipped with 100+ AI-powered checklists. These checklists span across industries such as QSRs, Drive-thrus, Cloud Kitchens, Restaurants, Hotels, Retail, and Manufacturing. In addition, the platform is compatible with all types of CCTV cameras and supports quick viewing, multi-device access, and robust remote assistance. With Wobot.ai, businesses can gain continuous feedback on processes, focus on areas of improvement, and highlight role models within organizations.

To use Wobot’s Video Analytics for your business, visit 

https://app.wobot.ai/signup

.

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7 Surefire Ways to Counter Labor Shortage and Improve Customer Experience

After the 2019 pandemic, businesses in major industries are already suffering from significant economic challenges. Although the receding effects of the pandemic have opened ways for companies to win the lost grounds, the new and modern pandemic is already waiting for them, the labor shortage.

Businesses across industries are facing a big labor crunch and finding it challenging to run day-to-day operations smoothly. This is causing a direct effect on maintaining customer experience and keep the business on the growth path.

As the vaccination is running in full force across the globe and markets are opening, there’s an urgent demand for labor to fulfill growing needs. For example, the demand for cooks in restaurants is expected to grow at 12% by 2026 needing 1,377,200 cooks compared to 1,231,900 recruited in 2016.

This piece will dig deeper into how businesses can overcome the growing labor shortage problem and improve customer experience. But first, let’s understand why the world is facing a labor shortage.

Why Is There a Labor Shortage?

Multiple factors trigger staffing shortages, and some of them depend entirely on the industry. However, some of the significant reasons for labor shortage are

·        Unemployment benefits

·        Reduced business capacity and local regulations

·        Domestic responsibilities

·        Health anxiety

·        Low wages

·        Smaller workforce

Ways to Overcome Labor Shortage and Improve Customer Experience using Cutting-edge Technology

The problem of a labor shortage is thick and needs urgent attention to reach closure. According to the statistics, 42% of small business owners couldn’t fill the jobs they wanted in 2021, making it the highest of all time.

As less staff is directly proportional to decreased customer experience, companies must develop ways that can enable them to overcome staffing crises and improve customer experience.

As technology is growing rapidly, businesses like yours must leverage various forms of technology assets to counter labor shortage. Here are some ways technology can help businesses tackle the labor crunch.

Line Busters

Many drive-thru restaurants face the problem of vehicle congestion that damages customer experience due to mismanagement. Labor shortage is one of major causes of mismanagement and that’s where line busters come to the rescue.

With line busters, your team can quickly access customer profiles, enter orders, and make payments to offer quick and high-quality service.

Tabletop Order and Pay

Richer customer experience is a key to business success. By implementing tabletop-accessible monitors help your team to serve more customers and focus on what needs their attention at that time.

These monitors help customers to order food and drinks, split bills, and more to quicken up the ordering process.

Labor Planning

Businesses need to keep their workforce happy with equally distributed work and ensure they are not piled up with unnecessary tasks and responsibilities.

Leveraging automation helps companies drastically to manage tasks like inventory and reservations without investing much. However, it might help you cut down the time of your skilled workers that can be used on other essential tasks.

Implement Flexible Shifts

The next thing you do as a business owner is keeping the morale and productivity of your employees high. Building flexible schedules by taking the input of your employees is key to success.

Distributing work keep flexibility in shifts will help employees manage work-life balance and keep them engaged in the work, focusing more on serving your customers.

Leverage the Power of Visual Intelligence

Technology nowadays is increasing and fulfilling the needs in the quickly shifting landscape of the industries. Solutions like Wobot.ai helps you increase efficiency by ensuring best practices and finding role models using AI-powered visual intelligence.

Implementing these solutions help companies do more with less investment and resources without sacrificing customer experience.

Voice-based Ordering

Voice-based ordering is another major way where technology plays the role of a savior in times of labor crises. It allows customers to order from a distance and therefore is used majorly in drive-thru restaurants.

It helps improve customer experience by improving order accuracy, enabling quicker transactions, and offering an overall joyful experience.

Improve Quality and Speed of Service

The best way to improve speed of service and improving quality is to empower your customers to self-order and pair it with exceptional on-demand customer service. Implementing tech pieces that can help ease the ordering process helps improve customer experience without depending more on human resources.

Conclusion

As the danger of labor shortage is not showing any signs of a slowdown, it’s critical to find alternatives to ensure customer experience doesn’t take the hit. Although job opportunities get a lot of exposure in the mainstream media, the labor shortage is a less-talked-about topic but affects small business owners severely.

Making the changes mentioned above helps ensure do more with fewer resources and delivers a high-quality customer experience. However, implementing new ways entirely depends on your creativity, and making changes to the operations as per your business requirements will help you successfully counter the labor shortage problem.

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How is Video Analytics Driving Industry 4.0

Industry 4.0 primarily refers to the fourth industrial revolution with automation in traditional manufacturing and industrial practices. Such a term was first introduced in projects conducted by the German Government in 2011, which emphasized computerization in manufacturing. The integration of intelligent machines, savvy computers, low-cost data storage, and transmission has paved the way for devices to interact within themselves and with humans to boost productivity and efficiency.

Why Industry 4.0?

The call for Industry 4.0 technology is characterized by creating self-learning machines to boost their overall performance and drive their maintenance efficiency. Other positive attributes include managing and effectively optimizing manufacturing processes, increasing the supply of products, and paving the way for intelligent manufacturing by bolstering productivity levels. In addition, with access to real-time data comes an opportunity to efficiently make smarter and faster business decisions that exceed human capabilities.

Additionally, Industry 4.0 technology would improve flexibility, agility, and profitability as the benefits of using AI lies in its reasonable costs and services compared to human labor. Furthermore, with automation happening everywhere, we are advancing towards an AI era where machines will make decisions and humans would supervise the whole process.

What is video analytics?

Video analytics is composed of two terms, “video” and “analytics,” which means performing an analysis of a video to achieve a result that could exceed human efficiency.

It is observed that when the data is received via real-time video, it then undergoes assessment. Following this, a response system is then activated to check whether the data is in accord. If there’s an anomaly, an alert is sent to concerned authorities immediately.

According to research data in 2019, the video Intelligence market stood at 23.6 billion US dollars. Moreover, the video analytics revenue is forecasted to be around 2.9 billion US dollars by 2022.

Video analytics offers facilitation in automating everyday processes and mitigating human intervention. It could also collect insights to suggest methods to drive your business and increase employee safety and security. Furthermore, with intelligent video analytics, it’s easier to perform root cause analysis and track employee’s productivity efficiently without human intervention. Given these factors, it comes as no surprise that industry 4.0 is gaining traction in the market.

Why video analytics?

Industry 4.0 is all about automating traditional industrial practices, and video analytics is an essential solution for that. With a robust video analytics system, production practices can be innovated to a whole new level, thereby transforming the entire manufacturing industry with its innovation involving both automation and reducing human intervention.

In the manufacturing sector, much of the work is mechanical and requires human participation. As a result, the safety of employees is of utmost importance in such an industry. Hence, by using video analytics, one can observe whether or not employees are adequately wearing the safety gear to work systematically in hazardous conditions.

Anticipating mishaps and alleviating the possibility of fatal injuries count as some of the more crucial variables of video analytics. Such instances become more likely if the employees are tasked with dealing with flammable liquids or heavy-duty machinery. An intricate video analytics system will monitor the position and location of the fire extinguishers that are to be used effectively during emergencies and notify the authorities of any workplace threats or mishaps. In addition, it helps you get first aid and medical responses for injured workers, alerts you if it detects any flames or smoke evolving, and predicts a fire.

Video Analytics with Deep Learning

Deep learning, a subset of machine learning, is a function of Artificial Intelligence. It learns and improves on its own by using complex computer algorithms. Then, it simply parses the data given, learns from them, and applies that knowledge to make intelligent decisions.

It is based on Artificial Neural Networks (ANN), a replica of networks in the human brain. It’s a complex network of nodes as neurons in brains. The more the network, the deeper the learning is done.

When video analytics is coupled with deep learning, it learns by the data provided to it. As a result, it can make intelligent decisions in an efficient manner that is beyond human capabilities. It can improve person recognition, work in ambiguous situations, and send alerts more efficiently.

The more accurate the data provided, the more precise the decisions are. For example, deep learning could mimic the human behavior of identifying if employees are wearing masks, social distancing norms are being followed or not, and send alerts for the same. In manufacturing plants, it could monitor if workers are wearing the safety gear, safety protocols are being followed or not, or analyze threats and accidents before they occur.

With the advances in deep learning, video analytics can now perform manufacturing tasks without human intervention.

How does video analytics work?

By real-time video or historical video footage, video analytics would work in 3 steps:

1.    Visual event modeling: In visual event modeling, complex algorithms detect and track events with an anomaly. It helps see any suspicious activity or something that’s not as per data and immediately alerts the authorities.

2.    Study of camera networks: Instead of individual cameras, there is an extensive network of cameras. Now, the same visual event data is collected more times from different cameras, ensuring the authenticity of data. Then this accurate data is parsed, a more precise decision is made, and system performance reaches its peak.

3.    Algorithmic and hardware design: The real-time video data would be more accurate when the algorithm and the hardware designs are in sync. It aids the video analytics software to make better decisions and learn from its own mistakes. Now it’s capable of analyzing both real-time and historically stored videos. It can predict a situation and act upon it by sending an alert.

Conclusion

Industry 4.0 will be the most exemplary industrial revolution. It will change the lives of millions of people with the integration of AI in different aspects of life. In the upcoming era of the technological revolution, video analytics would play a pivotal role in driving the fourth industrial revolution. Video analytics is expected to automate repetitive manufacturing processes and increase the efficiency of CCTV systems in the future.

By using intelligent video analytics, we can track almost everything, from customer data to manufacturing defects. Based on that data, we can provide suggestions to improve customer experience and establish brand loyalty. In upcoming years, businesses will need to be AI-powered. Video Analytics will empower us, humans, to work collaboratively with machines and make unanimous decisions to better the overall organization.

How can wobot.ai help?

Any video analytics software should be aptly designed to avoid errors and produce more accurate decisions. This is where Wobot.ai steps in. Our video analytics software offers a top-notch solution to businesses for ensuring safety, security and gain operational excellence.

Wobot.ai’s video analytics retrieve actionable analytics with deep learning that helps businesses get insights to improve operations to ensure safety and security. It transforms your existing CCTV camera infrastructure with the power of artificial intelligence to provide you deep insights. Try 

Wobot.ai today for your manufacturing business.