The Guide to AI-Powered Video Analytics

By Liz Oz • Jun 01, 2022

Computer vision for traffic analytics, traffic cameras detect parked cars.

Of the technologies driving innovation today, AI-powered video analytics is catching on fast. That's why the video analytics market size is expected to grow from 5.9 billion USD to 14.9 billion USD from 2021 to 2026 — a 20.9% Compound Annual Growth Rate (CAGR). By combining the deep learning capabilities of artificial intelligence (AI) with computer vision (CV) algorithms that give machines sight, AI-powered video analytics are unlocking new revenue streams across many industries. 

But what exactly are video analytics, and how do they help businesses grow? In this guide, we'll explore the landscape of AI-powered analytics and the benefits they bring, the improvements they offer to the current state of tech, and how these changes will impact the future of business intelligence. As we'll see, challenges exist in integrating them into existing digital frameworks, but they can be overcome with an efficient video analytics platform. Once they are, a trove of data insights is revealed. 

What Are Video Analytics?

Video analytics are defined as technologies that extract meaningful data from video footage, usually by identifying and/or tracking people, objects, or events. While they once entailed manual video analysis like a security guard monitoring video footage, they now involve the use of advanced algorithms. These algorithms detect digital patterns in images and decide how they should be processed. Once an object is detected, it can be classified, monitored, or followed as it moves through the frame. 

The data gathered from video analytics can yield a number of insights, such as:

  • Dwell time: to gauge customer interest and optimize store layout. 
  • Pass-through rate: to determine traffic flow, which can be useful for urban planning.
  • Feature recognition: to identify potential threats and boost security.
  • Object classification: to detect abnormalities and identify defective parts.

However the data is used, the end result is automated efficiency, real-time insights, and deeper business intelligence that only AI can provide.

Shoppers in a grocery store

How Computer Vision Powers Intelligent Video Analytics

Video analytics systems that involved a human evaluating the images or footage were tedious and offered limited insights — until computer vision (CV) came along. As the field of AI that enables computers to see, identify, and describe objects in an environment, CV has taken video analytics to new heights, especially when combined with other innovations. Two of them are:

  • Deep neural networks (DNN). This form of machine learning uses feature hierarchy across multiple layers to perform advanced mathematical modeling and gain intense knowledge of the object it studies. It is used in CV to recognize digital patterns with unprecedented accuracy. 
  • Edge computing. As opposed to cloud computing where visual data is stored, processed, and sent back to the site for action, edge computing enables real-time decision making by decentralizing computation to the camera itself. This type of processing is more cost-effective and offers greater potential - which is why edge CV is expected to grow faster than any other tech in the analytics market. 

While edge CV is preferred for its speed, sometimes a hybrid model employing both cloud and edge computing is preferred. In either case, the advancements made by CV when coupled with other technologies have led to a more efficient process that can give companies a clearer picture of the physical environments around them.

What Are the Benefits of Video Analytics?

Improvements in CV, AI, and the edge are making video analytics more accurate and efficient than ever before. This enables them to have an even greater impact on a wide range of industries, from security to retail to transportation. Here are a few benefits AI-powered video analytics bring to the table: 

  • Business automation. Manually extracting operational business data from video footage has historically been a slow and laborious process. While older methods often forced decision-makers to base their actions on data that was days or weeks old, modern video analytics allow users to automate their data collection, making the process more efficient and in real-time. 
  • Real-time alerts. Once business processes have been automated, companies can act on their data in real-time. From faster response times and greater situational awareness to detecting growing queues and responding with additional help, real-time insights from video analytics enable businesses to swiftly react to shifting customer demands. 
  • Better BI. The neural networks and ML algorithms behind video analytics systems are capable of a deeper data dive than legacy systems. Whether it's activity tracking at certain times, a heat map and dashboard creation, or historical data analysis, AI-powered video analytics improve business intelligence and make companies smarter.

Other benefits like greater quality assurance, improved security, and more accurate predictive analytics are also made possible by modern video analytics. Others, like empowering autonomous vehicles, are still in the early stages of being realized. 

Watch this video to learn how computer vision supports automation and boosts ROI:

What Are the Challenges of Video Analytics?

For all of the benefits they bring, designing and deploying effective video analytics systems doesn't come without challenges. Some of the biggest hurdles to clear are:

  • Environment. Many video analytics systems are installed in complex environments, involving detailed layouts and many moving parts. Creating an intelligent camera network requires an understanding of the use case so that data can be captured from all necessary visual fields.
  • Expertise. Video analytics applications are highly sophisticated, and often require a great deal of expertise to launch. Model training, edge deployment, and knowledge of DNN models are just a few components that developers must have fluency in to make these systems work.
  • Expense. From the cost of a custom solution to infrastructure, maintenance, and equipment, video analytics projects have multiple expenses attached to them. Careful planning is needed for successful deployment, and the development journey can sometimes belong.

Despite these challenges, the investment pays off for companies that make the commitment. Thankfully, there are computer vision platforms that greatly speed up development times and provide an easier, more accessible solution that brings higher ROI.

Check out this blog to learn more about the capabilities of a computer vision platform: What is a Computer Vision Platform? Complete Guide

Unlocking AI-Powered Video Analytics

AI-powered video analytic systems can be configured in a number of ways, but the general process follows a similar progression. The input consists of images captured by cameras and sensors, and they are processed by a series of algorithms that teach the system to locate and identify certain items or events. Once those items or events are detected, the system classifies the object accordingly and makes a decision on the next operation to be performed. A few of the basic techniques include: 

  • Object Recognition. A general term for a group of tasks that identify objects in an image or video, object recognition returns proposed object regions within an image and may attempt to classify them according to shared digital features. 
  • Object Detection. Building upon object recognition, object detection attempts to determine the number of instances of an object and assign them to a predefined class. This is often done through the addition of a bounding box, which specifies the area corresponding to the input image and assigns a confidence value that the object belongs to the class proposed. 
  • Object Tracking. In dynamic environments, object tracking can be used with object detection to follow an object's motion as it travels through the frame — and even into the frame of another camera. 

Facial detection and facial recognition are other techniques commonly used. While the two terms are sometimes used interchangeably, there is a difference between the two. Both employ object detection frameworks to classify and locate objects within a visual field, but facial recognition identifies individuals based on the characteristics they find and facial detection only locates them. 

While the difference may sound abstract, the applications where each is used vary greatly. For instance, retail stores wishing to track traffic flow may only need facial detection, but security applications wishing to identify intruders require facial recognition instead. 

Object detection detects traffic lights, people, cars, street signs, and a dog

What Are the Business Applications of Video Analytics?

AI-powered video analytics deliver operational benefits to many industries. A few of the most common applications are:

  • Retail - Customer experience, inventory tracking, people counting, layout planning, product placement
  • QSR - Increasing throughput, improving the operational efficiency of in-store and drive-thru locations
  • Security - Intruder detection, biometric security, automatic alerts
  • Construction - Productivity and labor management, theft prevention, building inspection, safety alerts
  • Transportation - Accident prevention, heat mapping, Automatic Number Plate Recognition (ANPR)
  • Smart cities - Incident detection and traffic monitoring
  • Manufacturing - Anomaly detection and quality assurance, remote troubleshooting, robotics 

Applications like autonomous vehicles and augmented or virtual reality are also being explored — and some frontiers are yet to be imagined. 

computer vision detects passengers inside a bus

Opportunity for New Business Value 

AI-powered video analytics not only gives businesses real-time insights and boosts their operational efficiency, but they also open entirely new opportunities for growth. By connecting them with IoT, cloud, and edge computing, video analytics empower companies to accelerate transformations and harness the full potential of their data. But, this technology is not without its concerns. 

Ethics and privacy concerns regarding tracking and consent have slowed the widespread adoption of this technology, but the rise of ethical AI will likely help overcome these challenges. Partnering with an expert in AI-powered video analytics like alwaysAI can help organizations adopt video analytics strategically and ethically while still accelerating their ROI.

Drive ROI With AlwaysAI 

Our team of experts at alwaysAI has extensive experience in video analytics, and we're looking forward to helping your business grow with this powerful tool. Our easy and affordable computer vision platform allows any company to quickly build and deploy custom computer vision applications that drive huge ROI. For more information, contact us for a demo or sign up for a free account today.

By Liz Oz • Jun 01, 2022

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