The growth of visual content within the digital universe is not slowing down anytime soon. According to a study by Cisco, by the end of 2022, videos will make up over 82% of all consumer internet traffic — 15x higher than in 2017. Organizations are beginning to be flooded with visual content daily.
Yet, it is still rare for companies to factor the deluge’s impacts into business strategy and account for the related shifts in consumer perceptions and behaviors. Enter image and video analytics. This article will discuss AI and deep learning-powered image and video analytics and how you may leverage them to improve business performance.
What are AI and deep learning-powered image and video analytics?
AI and deep learning-powered image and video analytics technology use artificial intelligence to recognize objects, scenes, and other elements within images or videos and is sometimes called computer vision. AI-powered image and video analytics also help detect, analyze, categorize, and tag images/videos. You can use it to perform tasks like:
- Identify the type of object within an image
- Detect faces in live video streams
- Analyze facial expressions in videos
- Identify products in photos or videos
- Determine whether particular objects appear together in images or videos
How do AI and deep learning-powered image and video analytics help businesses?
AI and deep learning are becoming mainstream as they permeate every industry with new applications and use cases. It is not uncommon to hear how AI helped diagnose a rare disease or find a rogue actor through facial recognition. But what about the less glamorous parts of the business? What about day-to-day operations?
Image and video analytics have become an integral part of enterprise IT infrastructure. There is no denying that images play an essential role in almost every industry today — from e-commerce to financial services to healthcare. Businesses need to be able to analyze images to gain hidden insights and ultimately make the most informed decisions.
The current state of image and video analytics tools is relatively nascent compared to other forms of data analytics like text or numerical data. This is because image processing can be more complex than dealing with text or numbers. Images have a mixture of visual elements like colors, shapes, dimensions, etc., that lend themselves better to machine processing than a manual process since a machine can quickly analyze pixel-by-pixel.
Deep learning can detect things like faces, objects, and even emotions in images with an extremely high degree of accuracy. Companies such as Google, Facebook, and Amazon have already used this technology for years to power many of their products and services. Deep learning models can provide automated analysis capabilities for any type of image data — be it medical images like x-rays, MRI scans, or satellite images.
AI-powered image and video analytics are used across various industries, such as BFSI, retail, travel, automotive, manufacturing, and healthcare. The technology helps businesses to:
- Identify important keywords in videos. Identify the most relevant keywords in your videos to determine how to connect with the audience most effectively.
- Facial recognition. A classic application that has been around for a while but is still very powerful. We've seen this used in Facebook's new face filters feature and Google Lens, which can also identify things like flowers and plants.
- Object recognition. An area where deep learning shines. Object recognition can be a powerful tool for inventory control, security, automation, and countless other uses. Object recognition is the ideal solution for monitoring personal protective equipment (PPE) usage in manufacturing facilities to increase employee safety. Applications trained to detect helmets, vests, goggles, face masks, or harnesses can be deployed in a facility to ensure employees are wearing the correct PPE. The system can provide alerts if an employee wears the wrong device. Another interesting application of object recognition is an entirely contactless checkout system. Through sensors and cameras powered by computer vision, a shopper simply picks an item from a shelf, and AI is used to create a “virtual” shopping cart for that person. Shoppers can pay for these items at contactless kiosks or simply walk out the door with a fully automated checkout system.
- Understand audience interaction with videos. Understand how people watch a video, where they pause, and what moments drive clicks or engagement. This data can help improve ad targeting and ensure your content is relevant to the right audience.
- Detect emotions through facial expressions. Analyze facial expressions and automatically detect a customer's emotions in images or short video clips. Recognizing emotions helps you better understand your customers' needs and desires and improve customer experience management practices in retail stores.
- Insights. Where AI excels since it allows us to understand what's happening in a video without watching from start to finish. AI can analyze frames or segments of the video and extract valuable insights using machine learning algorithms. The technology can analyze city traffic patterns, detect traffic jams, capture footage of accidents, analyze potential hazards, etc.
Some interesting applications of deep learning-powered image and video analytics
Businesses are increasingly using AI-powered image and video analytics to understand sentiment within their content, which helps improve brand perception and increase engagement.
Some interesting applications of deep learning-powered image and video analytics:
- Image recognition for retail analytics. Retailers can use image recognition to analyze hundreds of thousands of images taken by store cameras and identify customer interest in specific product categories or brands. The technology can also detect shoplifting incidents and identify patterns in theft behavior to improve store security and surveillance.
- Video analysis for insurance claims processing. Insurance companies can use video analytics to automatically detect accidents captured on dash cams or security cameras and classify them as rear-end collisions, rollovers, or sideswipes. This process can reduce the manual effort required to investigate and process claims while improving accuracy compared to traditional methods that previously relied on human experts.
- Diagnosis of diseases. Deep learning algorithms can diagnose cancer from images of patients' tumors and predict how likely new patients are to develop certain diseases based on their symptoms and medical histories. The technology can help with healthcare fraud detection, where it can spot anomalies in claims data that indicate the presence of fraudsters exploiting loopholes in insurance policies.
- Monitoring the shop floor. There are a variety of ways that manufacturers can use deep learning algorithms to analyze factory floor photos taken by industrial robots on an assembly line. These cameras typically capture hundreds or thousands of images per second as they move around a production facility. AI-powered image and video analytics can detect anomalies within the images and alert human operators if something goes wrong such as spotting a defective part, identifying a worker who is not following safety protocols, or detecting when there's an issue with the equipment.
The benefits of deep learning-powered image and video analytics for businesses
- Security. Identify potential security threats such as people flagged by law enforcement agencies, individuals carrying weapons or suspicious packages, etc. This identification helps security officers respond quickly to potential threats so they can act quickly and deal with potential issues before they escalate into a major incident or disaster.
- Enhance customer experience. Deep learning-powered image and video analytics can help enhance the customer experience. For example, if a customer walks into your store, you can use video analytics to recognize and greet them by name. This could be particularly useful for restaurants or hotels where customers stay for extended periods. You might also want to offer personalized recommendations based on previous purchases or browsing behavior.
- Improve operational efficiency. Another benefit of deep learning-powered image and video analytics is that it can help improve operational efficiency. For example, you could use it to automatically detect any flaws in your products or packaging that might need to be corrected before they potentially reach customers. You can also use it to identify problems within your supply chain and take corrective measures before they become an issue for customers. This could be useful in any industry where products are shipped worldwide or sold through eCommerce platforms.
- Reduce customer churn rate. Measure customer experiences by analyzing photos or videos uploaded by customers on social media platforms or review sites. This helps identify customers who are unhappy with their services. The business can then address issues quickly and before it is too late.
- Increase revenue. In real-time, image and video analysis can help businesses identify potential leads based on social media posts. For example, suppose a user posts an image holding a particular brand item. This might suggest that they could be interested in purchasing more items from the same brand.
An explosive amount of data in the form of images and videos is currently being underutilized. Companies that take advantage of the insights drawn by computer vision technology can gain a tremendous competitive edge that translates to the bottom line.
If you are interested in learning more about the application of AI and deep learning-powered image and video analytics, email us at firstname.lastname@example.org.. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with 2 TM. TM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.