Cameras are no longer just for security

Cameras are in use today, not just for security but for improving quality of life, anonymously

It seems you just can’t escape a camera these days.

Rewind to the past…the camera pointed at you was, more likely than not, a surveillance camera aimed at detecting crime or for some form of monitoring, or a security camera that was part of a system for ensuring public safety.

Come to the contemporary world…the camera is more likely to be collecting data that will end up being used anonymously in multiple ways to make your life simpler and more enjoyable.

Anonymised Data

“Anonymously” might sound negative, but here it actually refers to the fact neither the system nor the camera has to know who you are or what you do. When it is serving a good purpose and things are done in compliance to data privacy laws, what matters is that such data capture can be immensely society-friendly if the intent is good.

Take a simple example. A metro rail system may be using Cameras, powered by Artificial Intelligence and Computer Vision, to analyse traffic congestion on the platforms. The video analytics run on the data captured by the cameras may help the system adjust schedules or make operational changes that will enhance customer service. Data can be collected in such a way that it can never be linked to an individual, even if the software identifies the gender or age-group or the kind of dress he or she is wearing.

Elsewhere, the camera network on a factory floor may be simply helping to keep workers out of danger around active machinery or collecting data on their movement patterns. Today, by studying people’s behavior in several related situations, Computer Vision and AI help understand and predict human-machine interaction more accurately than ever before. Pro-active measures based on such data helps eliminate unsafe practices or work flow bottlenecks.

To demonstrate how cameras can be used to enhance productivity in factory floor
Cogniphi AI Vision used for enhancing productivity in factory floor

Technologies like Cogniphi AI Vision have enabled leveraging an existing security camera system to create dedicated video analytics platforms that benefit a wide range of industries, from manufacturing to retail to transportation to health to smart cities. If they are designed for Data privacy compliance and runs within a private and secure environment, these technologies can produce highly accurate and protected data that is committed to maintain anonymity. And they require very little hardware investment to deploy.

It’s the demography that matters

When a Video Analytics platform, say, tracks a Retail shopper, it can filter out personal data like facial features or how they walk, and focus on collecting and processing information like age and gender, buying patterns, time spent on comparing products, behavior and emotions at point of sale, and so on.

For the retailer too it is user behavior at a demographical level, more than unique identification, which will provide insights that matter to him. The store, for example, may want specific information on the type of shoppers usually passing through Aisles 5 and 6 so that it can plan targeted advertisements there aimed at a particular type of clientele.(For more info : AI Vision for Retail industry)

In the end, it is a win-win for both the retailer and the visitor. The retailer gets to understand customer behavior so that he can design better and more relevant promotions, marketing initiatives, A/B testing and performance tracking. The visitor gets overall a more interesting, pleasant and engaging experience.

Impact on RoI

Users of technologies such as Cogniphi AI Vision are often able to make decisive and impactful moves soon after they realize that the data thrown up by the software is consistent, reliable and accurate. Some of these insights might actually challenge several assumptions but they eventually lead to big gains, particularly when these products get to be used in multiple locations.

 Ideally the platform must be fully software defined so that capabilities can be added on later and data can be collected on a rolling basis when needed. It is when the customer gets this sort of flexibility that he will start relying on data for most of his decisions and feel the impact on RoI. 

Be aware of the practical side of AI implementations

Overcoming the Hype

Accessibility of useful data, heavier computing power and advanced algorithms have in recent years resulted in companies looking at AI to help drive efficiency, cut costs, increase revenues, automate routine tasks, improve employee experiences and understand their customers better. More and more use cases were also being conceptualized for strategy and innovation by a large number of corporates globally.

Major investments were made in the past across multiple industries to try and utilize data and AI strategically. However, several companies who initially scrambled to implement AI in their organizations did not eventually get to enjoy the benefits they had envisaged at the start. This lead to fears, somewhat justifiably, that AI is over-hyped. Of course it was over-hyped then.

AI-Ups and Downs

AI went though many ups and downs. It took several case studies and analysis of companies, people and systems deployed in AI projects to understand that the problem in most implementations was a basic lack of understanding of what AI is capable of, how to practically leverage data, and what the dangers are that need to be side-stepped to ensure real value for the business.

The fact that AI was hyped and why it was so has now created an appreciation of the need to implement it properly, but also a fresh awareness of its huge potential and how it can be a real value driver if done properly. Businesses today widely accept the fact that they must adopt AI strategies in order to compete. But they also need to beware of the pitfalls in going ahead without a clear cut plan or process.

How you Approach AI is the key

AI has proven its value across various sectors in multiple industries. While more and more use cases are being addressed by AI, an Accenture survey in 2021 revealed that most organizations are barely scratching the surface.

Even before the pandemic hit it was evident that many businesses were achieving significant RoI by rolling out AI beyond the pilot stage. During the last couple of years AI transformation became the very means of survival for many, whereas for others it became a catalyst for growth.

But what was the secret to these superior performances that have set new standards in AI implementation?

What set them apart is how these companies approached AI. They did not just adopt the Cloud and start using AI and data solutions. It is more about how they put a lot of thought into it, analysed the organization and the business environment and the pitfalls, and strategized before they actually tapped into any of the widely available AI technologies. The successful AI achievers were those that put people and systems first(digital transformation strategy), and technology later, and who looked at AI as a differentiator to stay ahead of the game.

The Strategy

First, do a holistic research and determine what AI can and cannot do for you. What exactly do you want to achieve? Is it to solve a problem, or to tap an opportunity, or get new insights, or is it for a total reinvention of your business? How and where to start, and what are the difficulties? Pick the brain of a data scientist if you can associate with one.

Draw up a list of potential use cases and a roadmap including a long term vision. Priority could be the low hanging fruits, the easy wins. Most importantly, understand what type of data is available and where, and what are the additional types of information that needs to be tracked to implement the chosen use cases.

The Team

You may not have all the required skills in-house to implement AI. Building a team or finding an AI partner with the desired capabilities is another important step. In fact, building the right team with the same mindset and personal skills to work with the rest of the organization can be the most critical factor in the long run. Businesses need to understand that this is not an isolated project but an integration of a new and soon-to-be-scaled AI platform into an existing IT eco-system.

The quick MVP and Roll-out

Develop the solution to your first business problem quickly, ideally within 2 to 3 months. This MVP (Minimum Viable Product) should have enough functionality, value and benefit for the user to sustain its usage and also a feedback loop to enable further development. Engage an expert who is savvy both technically and in the business sense. Sit with him and define the key performance parameters over the initial roadmap period. Review these regularly and modify them if necessary down the line.

After the MVP has demonstrated its value and been evaluated, prepare to rollout on a broader scale. Utilize your AI partner’s expertise and experience to keep in perspective both the technical and business factors during the implementation stage.

Addressing some typical problems

Organizations heading for AI implementations must give due consideration to these important issues they will face for sure.

Reluctance to change – This is an age-old management problem, but for AI implementation it refers to changing your decision making process into a data-driven one and not just based on instincts. To overcome this, some visible and measurable benefits have to be revealed early. That is where a quick MVP in a particularly relevant use case will help.

Lack of involvement – The users need to feel they are part of the solution and not being forced to adopt something that is being imposed on them. The environment has to be such that they are well taught and feel themselves to be joint architects of the project. Set realistic expectation levels and a roadmap for achieving results. Support of key management personnel and stakeholders are also critical for creating the right environment.

Quality and availability of data –No doubt that clean data is a key factor. So, centralized monitoring and control to obtain incoming data in a standardized format is absolutely necessary. There is also the challenge to locate and keep track of the right data and how and where it is stored. Do not go for complex machine learning models and stick to simpler solutions, if you do not have enough good data or sufficient labeled data that can be used to train the system.

Skillsets and Infra for implementing AI – It may be less challenging and cheaper to outsource than form a huge in-house team. In any case you need to do an evaluation study of what’s available internally and also define the costs going forward, keeping scalability also in mind.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions.  Cogniphi’s Vision Intelligence platform AI Vision integrates Computer Vision, Machine Learning, and AI to extract precise and meaningful data from visual footage. These are further converted into actionable insights and notifications.

References

https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021

https://www.gartner.com/en/articles/cfos-here-are-4-actions-to-ensure-you-implement-ai-the-right-way

AI Vision to Sky-rocket your Marketing Engagement in Retail Stores

With the increasing adoption of digital devices and the internet, the number of digital buyers has been increasing significantly each year. In 2020, more than 2Bn people purchased goods or services online and e-retail sales surpassed $4.2 Tn worldwide. With the lockdowns in place, retail e-commerce sales grew more than 25% globally.

Looking at these statistics, one may argue that online shopping has made customers way too comfortable skimming and buying products from the comfort of their couch – leaving them uninterested in going back to brick-and-mortar stores.

However, the reality isn’t so. Shoppers like seeing the product with their eyes, holding it, and experiencing it before buying it. So, retailers to improve their in-store marketing engagement in order to keep them coming back.

Through innovative AI insights, marketers and store planners can showcase products in places where the consumer is more likely to be compelled to buy it. Wondering how that is done? Read on to find out.

Win hearts with demographic intelligence

Imagine directing first-time visitors to exactly where they want to go without any assistance, to be able to know precisely what part of the store they’ll enjoy visiting the most. With AI Vision, you can gain demographic insights and intelligence to direct customers to the specific parts of the store that their peer group enjoys the most.

 With your cameras, identifying and grouping similar people and behaviors, it becomes extremely easy to predict behavior and leverage it to provide a more nuanced and personalized experience to the shoppers, making them more likely to come back.

Experience ROI with hyperlocal campaigns 

Another advantage is that by identifying the shopper demography and behavior, it is easier to optimize your hyperlocal marketing campaigns store by store in real-time.  This is helpful because ads that may work in a specific area may not be as effective in another. This will ensure hyperlocal messaging specific to your target audience. Identify what else your retail location could offer to better suit a customer segment’s needs with an AI-based analytical approach that leverages person-level metrics. This will allow your business to track profitable customers and their preferences. By determining what these customers prefer and how they behave, your organization will be able to improve its messaging to this segment. As a result, conversions from high-value customers will increase. 

Watch your buyer’s steps

Facial recognition, combined with demographic intelligence, can help you customize in-store advertising based on the audience while also providing valuable insights about what works and what doesn’t.

Similarly, AI Vision can enable footfall tracking to help you trace your customer’s footsteps around the store, picking up critical information. For instance, the dwell time on specific passageways, dwell time on customer engagement with ads and displays, average customer count on weekdays and weekends, the effectiveness of in-store marketing campaigns, etc. Based on this data, recommendations are shared with store managers and visual Merchandisers on the customer type, ad preferences, ad types,  placement, and time of display that will attract and influence shoppers. The effectiveness of these recommendations is measured and by applying continuous learning of AI Vision models, the recommendations are fine-tuned to attain maximum customer engagement and conversions.

Footfall tracking isn’t just a tool to measure and interpret your buyer’s data, it also imparts particulars. These particulars include conversion rates of unique customers, returning customers, customers leaving within 5 minutes (bounce rate), and so forth. All this data is sufficient to polish your in-store marketing efforts and predict stock demands and avoid stock-outs.

Intelligent experience for Intelligent Visitors

Consumers are looking for a smart, swift, and time-saving shopping experience while sellers are looking for buyer conversions or brand impact on a shopper’s mind.

 Improving their in-store marketing engagement and the ability to accurately examine the same can bestow some skyrocketing results. The power needed to kick-start these results reside within the capabilities of the computer vision applications currently being used and the opportunities to bring innovation and enhancements to them.

In some time, AI Vision won’t sound so unreal because it would be everywhere. Stores that take the leap and become early adopters of the technology would not only be striding ahead of the competition. They’ll also have enough real, on-site technology to customize it and innovate as per their unique needs to stay ahead of the competition even when they adopt it.

About Cogniphi

Cogniphi is a technology company that focuses on building next-generation vision intelligence solutions that are outcome-driven and seamlessly integrate into the existing infrastructure. Cogniphi’s AI Vision is a platform that’s built to improve operational efficiency at every level of an organization, across industries and sectors.

If you’re wondering how Cogniphi’s AI Vision can help you transform your business, get in touch with us for a free demo!

How AI Vision is Changing Public Safety for the Better

As we build a better society, one of the most important measures of progress is public safety. Companies around the world are designing innovative technologies to detect and prevent crimes. With AI, it has now become easier to leverage historical data to identify patterns and predict locations where crimes are most likely to happen in order to focus all energies into making the most vulnerable areas safe.

Moreover, with technologies like facial recognition and validation, it has become possible to solve crimes with reduced time and effort. More and more improvements in law enforcement can be seen with the implementation of data-based technologies, especially Artificial Intelligence.

But what’s in store for the future of law enforcement?

Vision Intelligence For Public Safety

Vision intelligence has highly practical applications in surveillance and public safety. With the ability to detect patterns in visual feed – images or videos, it becomes easier to track and identify items, patterns, people, and behaviours to trigger an alarm or warn the responsible security personnel in real-time of impending threats.

Some of the vision intelligence applications that are most useful in public safety are:

1. Facial Recognition and Validation

Facial recognition is currently a relatively commonplace technology where most smartphones are now locked/unlocked with it. However, it’s application in surveillance and public safety is more profound, and paramount. With AI-based visual intelligence, facial recognition can not only identify people behind a disguise but also read emotions and expressions to predict suspicious behaviour if and when a person is intending to commit a crime.
Moreover, with facial validation, it becomes possible to detect and prevent bypassing of facial recognition through identity theft or deep fakes.

 

AI Vision

 2. Behavior Detection

When trained to identify a set pattern of behaviour that people are required to follow, an AI-based camera can detect anomalies from the set pattern and provide alerts or notifications. This can be implemented in controlled public places such as prisons, mental asylums, etc. where a set pattern of behaviour is expected from individuals and any anomaly can be a sign of trouble.

AI Vision

3. Skeletal Construction

Certain movements can be identified in individuals that indicate criminal intent. In surveillance, with video cameras ingrained with vision intelligence, one can identify if there’s a fight that breaks out in an alleyway and whether it’s just a quarrel or it has serious repercussions. This insight could potentially enable the surveillance to make the right decision at the right time to prevent escalation of the situation and possibly prevent a crime from happening.

AI Vision

In addition to all this, the applications of vision intelligence are becoming more pronounced and imperative as we advance in technology and operations.

We, at Cogniphi, are building effective vision intelligence solutions that are outcome-driven and significantly improves the existing cameras in an area to drive insights and ensure improved surveillance and safety.

About Cogniphi

Cogniphi is a technology company that focuses on building next-generation vision intelligence solutions that are outcome-driven and seamlessly integrates into the existing infrastructure. Cogniphi’s AI Vision is a platform that’s built to improve operational efficiency at every level of an organization, across industries and sectors.

If you’re wondering how Cogniphi’s AI Vision can help you transform your business, get in touch with us for a free demo!

Why AI Vision is Crucial in Quality Inspection

The Fourth Industrial Revolution or Industry 4.0 is here – it’s blurring the lines between the physical, digital, and biological worlds. With a fusion of progressive technology like AI, robotics, the Internet of Things, 3D printing, and more, it’s changing the way the world functions.

We are witnessing the inception of full automation across sectors – manufacturing, retail, pharma, medicine, and more. Data has become instrumental to making this happen and Artificial Intelligence is what’s driving it. As we progress into a more capable, more exciting future, it’s imperative that the quality of production, in all sectors, becomes better and better – it’s called progress for a reason.

AI has been providing the inputs for digital transformation but without the capability to understand context. The AI cameras currently used by most businesses require a human to look at the footage to add context in the detection and have a very low accuracy which makes them ineffective. Without context, there’s little value that these cameras can provide.

The machines that businesses and organizations have setup into their premises to monitor data, especially cameras, only look at the 1s and 0s of the data that’s presented to them – they lack the clarity of context that denies them the opportunity to be precise, accurate, and error-free.

And that’s about to change.

In the last few years, we’ve taken huge leaps in vision intelligence. This prominent technology enables cameras to look at visual feed as a whole – complete in its context. This empowers them to identify, detect, or distinguish between objects with better clarity, precision, and accuracy.

Current advantages of vision intelligence in quality control

With the recent advancement in vision intelligence, we have already seen the benefits in quality control like:

  1. identifying damaged/faulty goods during production
  2. enforcing PPE for workers
  3. checking on vacant shelves in supermarkets
  4. Identifying and eliminating repetitive tasks

Why AI Vision will become crucial for quality inspection

Quality control is fast becoming automated in a lot of companies across sectors.

Such improvements in the process not only improves the productivity of a process but also leads to more efficient production leading to better returns for businesses with a higher production at reduced costs.

For instance, a camera overlooking a production line must have a person sitting behind it, ensuring that no product is damaged or defective. Now, this person could easily miss a few defective products owing to fatigue which could affect the later production. With an AI-enabled camera, the defective products can be instantly and consistently flagged and pulled out of the production line, saving wastage any further in the process.

These intelligent cameras can not only detect imperfections but also enable geometric inspection, packaging control, product classification, and more.

AI Vision

AI Vision

AI Vision

AI Vision

Some of the undeniable advantages of vision intelligence in quality inspection include:

  1. Being precise and accurate in reporting and flagging inefficiencies
  2. Accelerating the production speed through seamless flow of repetitive tasks
  3. Reducing the downtime of a process by eliminating breaks or rest
  4. Lowering the operational costs by cutting down on manual labor and saving on wastage

Cogniphi is accelerating the future of vision intelligence with Cogniphi’s AI Vision by enabling leaders in the manufacturing with specific cognitive intelligence to solve problems. We take a very objective approach to solving business problems – analyzing the challenge to find out the reasons behind any inefficiencies and reaching the root of the cause and solving it from within to ensure that the return on investment is profound.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.

 

How Predictive Insights Can Help You Reduce Operational Waste

Predictive analytics refers to a category of advanced analytics that can predict future outcomes with the help of historical data as well as data mining techniques, statistical modeling, and machine learning. Businesses use predictive analytics to detect patterns in their data to recognize opportunities or threats.

As interesting as this might sound, there is a vast ocean of unchartered territories and unclaimed knowledge that takes possibilities of predictive insights beyond tomorrow. Learn more about how companies can benefit from streamlining their operations to reduce waste with the help of this ingenious technology.

Ready to dive in? Let’s get right into it!

What is meant by Predictive Insights?

Predictive analytics is generally correlated with data science and big data. Businesses at present have heaps of data across equipment log files, transactional databases, video, media images, sensors, or various sources of data. To achieve actionable insights using this data, analysts and scientists take the aid of machine learning and deep learning algorithms to determine patterns for predictions regarding future trends and events.

These can include nonlinear and linear regression, support vector systems, neural networks, and decision trees. The findings obtained using predictive analytics can then be applied to prescriptive analytics with the goal of driving operations depending upon these predictive insights.

The History and Present Milestones of Predictive Analytics

Even though predictive analytics has been used by businesses for decades, the time to exploit it correctly has just begun. An increasing number of companies are adopting the use of predictive analytics as a way to boost their bottom line and reap the benefits of a competitive edge.

The question that arises is – why is it starting to peak now?

There are several answers to this question:

  1.  Increasing quantity and types of data, or maybe, an increasing interest to utilize data to extract actionable insights.
  2. Quicker and more economical computers are available today with huge computing power.
  3. User-friendly software.
  4. More challenging market scenarios present the requirement for competitive segregation of products and services.

With the rise of interactive and user-friendly software in all markets and industries, predictive analytics is not just a domain for mathematicians or statisticians anymore. This niche is now free of complexities and can be used by business analysts or business experts with ease.

What Makes Predictive Analytics Vital to Reducing Operational Waste?

Companies are rapidly adopting predictive analytics to sort out their existing problems and reveal better opportunities. The common benefits of predictive insights to operations include:

– Fraud Detection: Gathering various methods of analytics can boost the detection of trends and patterns to prevent criminal conduct. With cybersecurity being at the forefront of businesses’ main concerns, the help of high-quality behavioral analytics reviews all the transactions and processes undertaken with suppliers and quality control in real-time. This helps them to find any irregularities that could point towards fraud, advanced threats, or zero-day vulnerabilities.

–  Improving Production Strategies: Predictive analytics are employed to understand the demand and supply chain of their production process. By monitoring their suppliers’ inflow of raw material, they can place orders well in advance, avoiding idle time or resorting to last-minute supplies that compromise quality.

– Efficient Production Operations: Many businesses employ predictive models to project inventory and control their resources. Predictive analytics helps businesses to maximize their operational efficiency and reduce rejects or sub-standard production by figuring out discrepancies in processes or equipment much before these breakdowns occur.

– Risk Reduction: The integrity and competency of the shop-floor staff matter a lot in manufacturing. With predictive analytics and insights, one can look up the reasons for the continual reject causes in their production jobs and batches. If human error is responsible for these wastes, they can be promptly addressed. If the insights rule out human error, it also gives manufacturers the chance to improve accountability, thereby lowering the risk of equipment malfunction or external factors like raw material quality.

How are Predictive Insights Used in the Manufacturing Operations

In the manufacturing industry, it’s crucial to find the factors that cause poor quality and failure in production processes. It is also important to optimize elements, services, and logistics. One of the most successful manufacturers that use predictive analytics to improve their grasp of warranty claims is Lenovo; their step forward led them to reduce 10 to 15 percent in warranty charges. Another benefit of using predictive analytics in manufacturing is that monitoring the equipment performance and yield quality can help track inconsistencies much before the quality checks fail.

Optimizing complex production networks

Predictive maintenance studies are devised to boost the operations and profitability of each production machine and process. Additionally, PPH maximization can streamline the interaction of the processes and machines used. Including all the processes, be it from the purchase of raw materials to the production process and sales, this superior technique of modeling exponentially improves the revenue in complex production operations and supply chains.

Unlike manual planning, advanced analytics generally considers about a thousand different variables and 10,000 constraints to assist the manufacturers to understand what to buy, what to manufacture, and how to manufacture in order to generate great profits throughout the year.

Achieving abundant profits with less

Just like predictive maintenance helps to enhance the uptime of any given asset, predictive analysis can improve its productivity and OEE. Even minute improvement in the overall operational efficiency (OEE) can dramatically boost profits before interest and tax (PBIT). This is achieved by enhancing the production output, material costs, and throughput to boost the profitability of every process involved.

Reduction in downtime in a busy environment

With the help of advanced analytics, manufacturers can boil down the causes of their equipment breaking down and control the input metrics so that they can fix an issue before the breakdown occurs or be prepared to quickly fix issues in order to minimize total downtime. Predictive maintenance can help to increase the equipment lifespan by 20 to 40 percent and reduce downtime by 30 to 50 percent.

Operations are linked closely with the metrics of production, reject (waste), and downtime, which can now be monitored and controlled better with the aid of predictive analytics. By projecting the future demands, manufacturers can be prepared for market shifts, optimize their processes and improve the overall efficiency of their sites using this sophisticated technology. We hope that this blog gave you valuable insight into the world of predictive intelligence and its link to operational efficiency.

What Is AI Vision and Why Is It Here to Stay?

Artificial Intelligence is one of the most misunderstood and overused buzzwords of our generation. We hear so many organizations bragging about their use of AI without really understanding what they are doing with it. In this blog post, our experts share how Artificial Intelligence is used in Cameras to improve business operations. Also, learn why it will become the future of how we do business.

Computer Vision is the application of Artificial Intelligence (“AI”) through a visual medium, like a live camera feed or an image. It essentially aims to replicate human perception and visual cognition. You must have already encountered computer vision without even realizing it, for example, the face-lock on your phone screen, to some level, is a simple application of computer vision or the Instagram or Snapchat filters that alter your hairstyle or turn your face into an animal on the screen, are all made possible with Computer Vision.

Any device that can comprehend a visual feed like a human being is a computer vision system, however, Computers have the ability to process more than humans can. Think about yourself reading these words. It’s likely that you’re focusing on one word at a time while you are reading, and recognizing the words around these words without fully processing them. Using Computer Vision, a computer would be able to see and read all the words on the page at the same time, drawing immediate meaning from the entire page

This principle also applies in a CCTV control room. Normally you would have people monitoring the live camera feeds on multiple screens, but how many live feeds can one person accurately monitor at a time? That’s where Computer Vision shines. It’s able to continuously and accurately monitor the feeds from all cameras simultaneously.

Automation integrated with AI Vision enables the workforce to overcome typical challenges of humans, like fatigue. It provides unseen insights that humans find difficult to access or comprehend in real-time. While human perception has its own share of limitations, a camera can gather every second of visual data from your premises that can be further analyzed to identify areas typically missed by humans.

Computer vision is the most natural next step in machine evolution. The purpose of AI is not to replace humans but to assist them. For that to happen, it needs to command similar cognitive abilities with enhanced capabilities. However, the underlying science behind AI Vision is not as complex or overwhelming as it might appear. Here’s a brief understanding of how AI Vision functions:

Capture Metadata from Camera Feeds

Content feed from a camera is analyzed by trained models to recognize objects, their actions, specific characteristics, and interactions in space and time. Typically, in a retail store, a camera equipped with Cogniphi’s AI Vision can detect shoppers, analyze footfall, recognize customers with face recognition, and evaluate customer expressions like anger with emotion recognition. Similarly in manufacturing, AI Vision can do myriad tasks such as identifying quality for inspection, counting warehouse inventory, etc.

Pattern Recognition and Anomaly Detection

Computer Vision derives relevant insights from unstructured data through contexts and occurrences of patterns along with their co-relations. In a retail store, AI Vision can analyze shopper dwell time, shopper interaction with products, alert anomalies such as misplaced objects in the wrong area, water spillage, and identifying reasons for inventory shrinkage, etc.

Recommendations and Predictive Analytics

Smart Vision equipped with actionable insights provides recommendations in the form of real-time alerts, analytics, and also integrates with existing business systems.

Cogniphi’s AI Vision is a pioneer in providing hyper-local computer vision solutions to retailers and manufacturers. Different components of the solution cater to different aspects of visual data and analytics to

– detect objects and their interaction in space and time.

– detect human body parts, their movement, interactions with real-world objects, their transformations, and other associated patterns.

– detect human faces and recognize people in 1:1 (Verification) and 1:N (Identification) mode.

– detect attention, emotions, and expressions on the faces

– identify and detect textures of objects.

– extract data from several documents and categorize data to train the model to interpret and analyze this data.

AI Vision is Here to Stay

Vision Intelligence technology is a cost-effective upgrade to existing data feeding cameras. On top of being a non-disruptive installation, Cogniphi’s AI Vision can help an enterprise reduce human error and experience overall increased productivity.

The Grand View Research states the growing valuation of the global computer vision market is expected to go up from $11 billion in 2020 to reach $19 billion by 2027. Manufacturing, Energy, Retail, Transportation, and Healthcare are the industries identified as best positioned to capitalize on this technology in the coming years.

The 2020 McKinsey Global Survey on AI has concluded that 50% of companies have adopted AI in one or other business functions and that the majority of use cases are aimed at optimizing operations or at product development or at customer service improvement. As companies increasingly look at AI to solve real-world challenges that depend a great deal on visual inputs, computer vision will have a huge role to play in achieving these objectives.

About Cogniphi

Cogniphi is a technology company that enables customers to achieve transformational outcomes through cognitive digital solutions. It believes in a 360-degree problem-solving approach, building solutions that can scale and adapt to changing business demands for continuous improvement.

Combating Food Waste using AI and Computer Vision

One of the biggest ongoing tragedies in our lifetime is the fact that millions of tons of food (estimated at 1.3 billion tons globally, annual) end up in the trash bins every year. It is not a geography-specific tragedy. Food waste happens across the world, its immensity undiminished whether it be Europe, America, Africa or Asia. The problem is so acute that the United Nations has put a target of curbing food waste by 50% by 2030.

Waste happens across the entire value chain, from farm to processing centres to distribution outlets to households to kitchens and to tables. Studies have shown that whereas kitchens and households are the areas contributing the most to food waste, it is the production and processing phases that account for 30% of the food being destroyed.

Artificial Intelligence a valuable tool

AI has been revolutionizing the food industry and has made it possible for companies to drastically reduce their food waste. Thanks to AI, these companies have become SMART through these two routes: Forecasting Demand more accurately to improve planning and production processes, and Detecting and Analysing Food waste Data for superior decision-making.

Better planning through AI-based Demand forecasts

Several data-based algorithms are already in use to improve production planning and process-efficiency, particularly to control excess production and aesthetic quality fluctuations that may depend on the raw material used. AI is also extensively used to forecast requirements, which has helped tremendously in reducing waste of perishable food like vegetables and fruits that spoil quickly or meat and other products that have an expiry period. It is now a proven fact that AI and ML methods are most effective in those functions that are predictable and controllable.

Production processes based on accurate forecasts and analysis of customer behavior obtained from such an IT eco-system help in optimizing inventory levels, expiry dates and time to market. The end result is less waste, less price fluctuations and more profits. As food companies and AI developers work together more, innovation and remarkable outcomes are bound to be plentiful.


Computer Vision for analyzing Kitchen Waste

Data of food that is thrown away is the most important information which can help in fighting food waste. Since manual collection of this data is not practical on a long term basis, cameras trained by AI software to identify and classify discarded food will play a crucial role in this battle against food waste in kitchens, hotels, restaurants and hospitals.

Waste bins are literally turned into AI-enabled bins that detect and record the discards. Information on weight and cost of discarded waste can also be captured by incorporating AI-powered scales. By helping kitchen management and policy makers to improve food preparation methods, revise menu card options and make better buying decisions, insights provided by the data can be the core for building critical applications to drive waste reduction.

The food industry is also working towards establishing a pool of AI algorithms that have been successfully implemented, so that more data gets shared across multiple users. This also helps the AI model to be trained on a continuing basis. It is gratifying that some of the top food corporates have pledged to share details of their successful programs that have enabled them to fight food waste, so that others can also benefit from them.