After evaluating several vendors, the company chose Indium Software to implement a cognitive analytics solution leveraging computer vision to achieve its goal. Indium enabled this by using the existing security cameras:

• To generate heat-maps across the store
• Using ImageAI for facial recognition, identify customers and count the number of customers who enter the store at a given time period
• Incorporate a customized functionality to allow the cameras to count the number of people who performed a particular activity – like walk to a particular section of the store

The data collected from these cameras was used to analyze customer behavior at specific zones within the store. This was done by building comprehensive dashboards with real-time data refresh.

Indium built a neural network model by training various classes of the object or person, using 2000+ images for image processing and video analytics with maximum accuracy. By creating and testing annotations using sample videos, it was able to constantly improve the accuracy (over time, accuracy went up to 80%). Within the data points that were gathered, outliers were identified.

Since the visitor statistics was linked to the POS systems, they were able to track the conversion rate, improve product placement and implement cross-selling tactics. The solution had an easy-to-use interface that provided all stakeholders with better insight into customers’ behavior as well as shelf zone analysis.

As a result, the retailer was able to experience a 15% increase in customer satisfaction.

Deriving insights using Cognitive Analytics

According a report published by MarketsAndMarkets, the broader market for computer vision-enabled solutions is expected to grow from USD 15.9 billion in 2021 to USD 51.3 billion by 2026 at a CAGR of 26.3%. Of course, this includes the use of computer vision for factory inspections, audits, quality and safety management, etc.

Computer Vision, simply put, refers to enabling computers to identify and process objects in images and videos the way a human brain does. It helps computers to “see and understand” content in photographs and videos. By integrating AI capabilities to computer vision, we’re now able to conduct analytics on data from images and videos.

Today, it can be used to identify an item with 99% accuracy as against 50 percent a decade ago.

There are use cases for cognitive analytics and computer vision in a variety of industries such as:

1. Energy, Power & Industrials: Computer vision can enhance safety, efficiency, and regulatory compliance of the power and energy industry by monitoring equipment for preventive maintenance and inspect linear assets such as power lines and pipelines for safety. It can also help with regulating danger zones and alerting in case an employee crosses a designated safety threshold.
2. Manufacturing: Computer vision is being used in production lines for audits, inspections and overall quality management. Not only can it be used to detect defects, but data from other cameras and sensors can be cross-referenced to identify the root cause of problems. This can speed up repairs and prevent expensive downtime. It can also be used to prevent mislabeling and shipping errors. Workplace safety is another area where it can be used effectively.
3. Retail: From improving customer satisfaction to inventory management, computer vision can be used in many ways to improve sales and profitability. Managing the store environment, enabling self-check-out, prevent shrinkages, and improve POS accuracy are some of the other ways in which it can be used.
4. Transportation & Logistics: Right from autonomous vehicles to managing transportation and logistics, computer vision can be used to streamline operations and minimize supply chain disruptions. It can help improve the safety and efficiency of transportation fleets by ensuring proper docking, loading, fueling, and tire pressure.
5. Healthcare: Medical diagnostics can benefit tremendously from computer vision risk assessment and early detection of disease. It can be used to ensure diligent conformance to safety practices such as handwashing among medical staff, manage inventory in pharmacies, ensure sufficient stocks in hospitals and clinics. It can also help in lowering administrative costs through automated document processing.
6. Legal Enforcement: It can be very effective in preventing crimes by scanning live footage from public spaces and detecting weapons or identifying suspected behavioral patterns.

Indium’s Comprehensive Solution for Cognitive Analytics

Indium is a technology solutions company with deep expertise in digital, data engineering, data analytics services. With its global presence serving customers ranging from innovative product startups, Fortune 100 and global enterprises, Indium’s key differentiators are its specialization in:

• Ai, Advanced Analytics & Text Analytics CoE
• Big Data, Data Engineering, Stream Processing & Data Virtualization
• Low-code development across platforms

Indium Software has a team of cognitive analytics experts who work on the following areas:

• Image classification and analytics
• Object detection
• Facial recognition
• Video analytics
• Text analytics
• Speech recognition

We use FCN (Fully Convolutional Network), Mask-RCNN (Region-based CNN), and Detectron2 for image and video analytics; and deep neural networks and Optical Character Recognition (OCR) for text analytics and speech recognition.

Our range of solutions includes:

• Room Type Classification: Convolutional Neural Networks (CNN), YOLO, VGG16 Is used to classify room type using a pre-trained model. Based on their characteristics, images are categorized into different room types.
• Room Object Type Classification: Convolutional Neural Networks (CNN), YOLO is used to build pre-trained models to identify images and tag the object type in the room. A set of images is used to improve the accuracy of the model items in the image and room classified with a bounding box drawn around.
• Facial Recognition: Indium uses a sophisticated, scalable face recognition system, Multi-level CNN architecture, to detect, recognize, and analyze human faces in images. The system can also detect different face-related attributes such as head position, facial hair, emotion, gender, and age among others.

To know more about how Indium can help you build your cognitive analytics model integrating it with computer vision, contact us now.

The post Indium’s Computer Vision and Cognitive Analytics Solutions for Better Decision Making appeared first on Indium.

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.