You would either apply brake and/or reduce the speed of the car. Thus, in a fraction of a second, the human vision has completed a complex task: of identifying the object, processing data, and making a timely decision.

That bit of detail helps understand the computer vision technology.

It is a field of computer science that enables computers to see, identify and process images in much the same way as the human vision before generating the necessary output.

The objective of computer vision is to enable computers to accomplish the same types of tasks as humans… with the same level of efficiency.

According to a report by Grand View Research, the global computer vision market size is forecast to grow at a compound annual growth rate of 7.6 percent between 2020 and 2027.

Advancements in artificial intelligence (AI), deep learning and neural networks have contributed to the growth of computer vision in recent years, so much so they are outdoing humans in tasks such as identifying and labelling objects.

The high volume of data being generated—an estimate is that 3.2 billion images are shared every day, to go with 720,000 hours of video—is another contributing factor which helps train and improve computer vision.

How computer vision works

Pattern recognition is the most important aspect of computer vision.

Therefore, one way to train machines to understand visual data is to feed labelled images and apply software methodologies or algorithms to help them identify patterns in those labelled or pre-identified images.

For example, if a computer is fed with tens of thousands of images of an object, it will use the algorithm to analyze the features and shapes to recognize the labelled profile of the object.

This is part of training a computer which, thereafter, will use its experience to identify unlabelled images of the object it was previously fed with.

Rates of accuracy for object identification and classification have increased from 50 percent to 99 percent in less than a decade, with modern systems proving more accurate than humans at detecting and responding to visual inputs.

Applications

Use cases of computer vision are not only limited to tech companies but the technology is integrated into key, everyday products for higher efficiency.

Self-driving cars

Computer vision helps self-driving cars understand their surroundings and thereby drive the passengers safely to their destination, avoiding potential collisions and accidents.

Cameras fitted around the car capture video from various angles and the data is fed into the computer vision software, which processes the input in real-time to understand the road condition, read traffic signals and identify objects and pedestrians en route.

The technology also enables self-driving vehicles to make critical on-road decisions such as giving way to ambulances and fire engines.

With millions killed in car accidents each year, safe transportation powered by computer vision is paramount.

Facial recognition

Computer vision algorithms identify facial features in images and correlate them with the database of face profiles.

The high volume of images available online for analysis has contributed to machines learning and identifying individuals from photos and videos.

Securing of smartphones is the most common example of computer vision in facial recognition.

Computer vision systems are adept at identifying distinguishing patterns in retinas and irises, while they also help improve the security of valuable assets and locations.

According to a NIST report, the leading facial recognition algorithm as of 2020 has an error rate of 0.08 percent, a remarkable improvement on the 4.1 percent error rate in 2014.

Medical diagnosis

Engineers at the University of Central Florida’s Computer Vision Research Center taught a computer to find specks of lung cancer in CT scans, which is often difficult to identify for radiologists.

According to the team, the AI system has an accuracy rate of about 95 percent, an improvement on the 65 percent by human eyes.

It essentially proves that computer vision is adept at identifying patterns that even the human visual system may miss.

Such applications help patients receive timely treatment for cancer.

Manufacturing

Computer vision helps enhance production lines and digitize processes and workers in the manufacturing industry.

On the production line, the key use cases are the inspection of parts and products for defects, flagging of events and discrepancies, and controlling processes and equipment.

Thus, the technology eliminates the need for human intervention on the production line.

Law

Computer vision enables the prevention of crimes by helping security officials scan live footage from a public place to detect objects such as guns or identify suspect behavioral patterns that may precede illegal and dangerous action by individuals.

The technology also aids authorities with the scanning of crowds of people to identify any wanted individuals.

What’s the future of computer vision?

Considering the modern capabilities of computer vision, it’s a surprise that applications and advantages of the technology remain unexplored.

In the future, computer vision technologies will be easier to train and they will also capture more information from images than they do now.

It is being said that computer vision will play a key role in the development of artificial general intelligence and artificial superintelligence by enabling them to process information on par with or better than the human visual system.

The post Top 5 Applications of Computer Vision (CV) appeared first on Indium.

However, today video analytics has moved on to being something much more – it has grown to be a tool that can be leveraged for business growth. The video surveillance market is set to hit $ 82 billion USD by 2025. The adoption of video analytics by retailers has increased by 16% year on year for the last 7 years.

Video analytics delivers value to retailers in many forms – in terms of measuring store performance, enhancing the customer experience, increasing engagement with customers and ensuring customer loyalty.

Many retailers face the issue of high footfalls but disproportionate point of sales revenue. The use of video analytics allows retailers to not only make decisions based on footfall data, but also gives them the liberty to drill down further and analyze the following:

• Whether consumers compared a particular product with other brands?
• Who are the returning customers?
• Who exited the store without purchasing anything?
• Whether they looked at an item or spent significant time in a particular area of the store?
• Whether they picked a product?
• Whether they bought multiple products?

Indium’s cognitive analytics capabilities helped one of the largest sporting goods retailers in the world to increase their customer satisfaction levels by more than 15%.

The client had 1500+ stores across more than 45 countries. Being a giant in the space and competing in the fast-moving retail market meant business decisions and market strategies needed to be flexible enough to change quickly as well.

To achieve this, there was a need for real-time store visitor analytics. Footfall data is what most companies use today to achieve this. However, the client wanted to step it up by linking footfall data with POS data. This needed a robust yet simple solution that generated accurate results.

The requirement laid out by the client was to effectively improve performance of the store and increase customer satisfaction. In order to meet the requirements, Indium had to:

• Leverage security cameras across the store to generate a store heat-map.
• Leverage CCTV camera data to understand the variations in footfall by area of the store and time of the day.
• Identify customers using facial recognition to know the number of customers leaving the store without making a physical purchase.
• Build comprehensive dashboards with real-time data refresh for better insight into customer’s behavioral as well as shelf zone analysis.

Seeing this as an opportunity to solve a difficult problem for our client with an out of the box solution, Indium implemented the following solution:

Solution Implementation:

Indium analyzed the video feed data collected from the cameras installed on the shop floor and built a cognitive analytics model solution that leveraged the data to meet the requirement:

• Firstly, Indium used ImageAI to empower security cameras to count the number of customers who enter the store at a given time period. In addition to this, customized functionality was incorporated which allowed the cameras to count the number of people who performed a particular activity – like walk to the cycling section of the store.
• A neural network model was built for image processing and video analytics. The model was analyzed and optimized in order to ensure maximum accuracy.
• Within the data points that were gathered, outliers were identified as the next step of the solution.
• Pattern recognition was in place around all the shelf zones.
• Even though this was a very complex neural network to build, there could be zero compromise on accuracy.
• Each layer of Neural Network was built by training various classes of the object or person, where 2000+ images were used.
• Annotations were created and tested using sample videos. This particular neural network model which was built would become more and more accurate over time – more the number of videos used, higher the accuracy level over time.

Business Impact:

• An accuracy of more than 80% was achieved in the models specifically built to target customer behavior. This ensured improved customer engagement and better targeting of customers.
• Comprehensive analysis on the conversion rate from visitor statistics to live sales via the POS systems, and analysis of customer interaction in any product section and the product-wise conversion rate, helped improve product placement and cross-selling across product categories.
• The client saw a solid 15% increase in customer satisfaction post implementation of Indium’s solution.
• The easy to use User Interface enabled all stakeholders to get a better insight into customer’s behavioral as well as shelf zone analysis.
• 70% cost savings in short and long-term as the tools used in the project were open source.
• Highly comprehensive dashboards were built with real-time data refresh, tailored to the client’s analytical and business needs.

The post Video Analytics with In-store CCTV camera feed for Sports Goods Retailer appeared first on Indium.