How Edge AI Cameras are Revolutionizing Tomorrow’s Vision Applications.


Smart product manufacturers push for integrated Artificial Intelligence video analytics.

Artificial Intelligence (AI) is a growing trend that has integrated itself into virtually every area of our lives. Not just in the professional realm with industrial IoT (IoT), robotics, automation, and more. AI-enabled applications have become an unobtrusive part of our daily lives. Edge computing will only boost this trend, moving AI workloads from the cloud to the intelligent edge for better response times, bandwidth savings, and data security.

In this article, ViNotion gives you a glimpse into the rapidly advancing world of Artificial Intelligence, image interpretation, and smart cameras, and explains how they provide OEM/ODM solutions for the development of products featuring the latest in AI camera technology.

Everybody’s talking about it. Artificial Intelligence, Video Analytics, Deep Learning, and Machine Learning and now Edge Computing too. But what does it all mean? To answer that, first let us walk through some of the jargon.

Artificial Intelligence

development is centered around enabling video cameras and systems to perform jobs that were only possible by using a human brain. Smart algorithms provide complex analysis and exploit Machine Learning (ML) to teach a machine how to interpret certain data. The demand for video analytics is growing rapidly because of the increasing strategic significance of AI in our daily lives and work. With the great potential of AI vision, many businesses and organizations are moving away from traditional video surveillance and automating the human I with computer vision. Computer vision is the overall term for automated image interpretation. It seeks to make sense of digital images, videos, and other visual inputs. Combining Artificial Intelligence with Machine Learning is a type of computer vision technique.

Video Analytics

is about extracting useful information from video recordings or live videos. Data output can be anything from counting the number of people in a video to identifying specific objects or people. Video analytics builds on computer vision, pattern analysis, and intelligence. It brings much-needed solutions to challenges across several industries and applications, such as surveillance, retail, and transportation analytics. Some call it video content analysis (VCA) or intelligent camera systems (ICS).

Deep Learning

is a special type of machine learning. It uses neural networks to learn patterns in data. Neural networks are composed of layers of interconnected processing nodes, very similar to human brain neurons with interconnections. Rapid advances in Deep Learning have already brought many benefits to video analytics, and this trend will continue as this technology is evolving rapidly. Deep Learning algorithms are being used to detect and track objects in videos and to recognize specific actions.

Real-time detection and tracking of objects in video streams are a myriad of computer vision tasks. It’s about detecting and tracking specific objects in a video sequence. Using a convolutional neural network (CNN) to learn complex patterns from data is one of the popular techniques. The software leverages such Deep Learning models to analyze video and detect and track objects for trained classes, such as vehicles, people, and traffic lights in real-time. Advanced video analysis software allows you to count objects and perform rule-based analysis, for example, to count people in areas with large crowds. It is helpful for better crowd management.

Computer Vision systems

apply image processing algorithms to multi-step computer vision pipelines for the analysis of images and the extraction of insights from video data. It is a broad and generic term for all image processing tasks It can do complicated and mission-specific tasks like finding objects, recognizing activities, checking quality, and more.

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Edge Video Analytics systems

can execute computer vision directly in-camera or through a connected edge computing system. Computer vision may comprise advanced Deep Learning algorithms. Many computer vision systems don’t work unless connected to the cloud. They generate a lot of image data, demanding a lot of bandwidth. It is putting a heavy strain on networks. In most cases, there is no need for so much of the data sent into the cloud. Tackling this at the source would be much more efficient. Many people are worried about privacy in the cloud. The phrase hacker may be a bit obvious, but our smart speakers and cameras of today capture a large amount of data that is inherently private. To analyze this data locally, sending only what’s relevant to the cloud limits the risk of attack and snooping. Even when low latencies are possible with 5G, devices that need to make decisions very quickly would still have to wait too long to get information from the cloud.

Smart cameras and intelligence at the edge

Edge-based AI Video analytics is enabling more and more products with integrated cameras, such as smart cameras and intelligent digital video recorders, to perform automated tasks that just a few years ago had to be observed by humans. Artificial Intelligence (AI) and Machine Learning (ML) are emerging in intelligence, adding value to video analytics in the form of insights, predictions, and alerts.

Artificial Intelligence video analytics is winning ground for analysis and automation in many industries. While technology is still evolving, MarketsAndMarkets  predicts that the global video analytics market will be worth $14.9 billion by 2026, growing at a CAGR of 20.4 percent. Artificial Intelligence (AI) and neural networks (NN), in particular, are emerging as key disruptive technologies with a wide range of applications in different markets and segments. Edge-based AI technologies have become increasingly available and affordable for camera vision applications. It enables a new class of smart cameras with advanced video analytics, abnormal behavior detection, object and people recognition, to name a few applications.

Recent innovations in video analytics inspire a new generation of intelligent cameras equipped with advanced video analytics technology. Built-in sophisticated technology is employed for detecting abnormal behavior, or object and vehicle recognition, for example. Indeed, quite a few new-age startups are focused on developing high-end cameras that use advanced technologies such as Artificial Intelligence and machine learning for better pattern recognition and surveillance. These companies have barely scratched the surface of the true potential of video analytics. With the increasing demand for cybersecurity, advanced video content analytics is expected to become mainstream quickly.

Smart cameras connected to the Intelligent Edge have led to an explosion of creative problem-solving, increased productivity, and enhanced safety and security solutions.

Smart cameras deliver so much more than surveillance as we know it. Though not all-encompassing, we can loosely categorize the applications for smart cameras into three categories: assistance with monitoring and counting, detection, and alerting. These capabilities, combined with analytics performed at the Intelligent Edge through AI and ML modeling, enable action to be taken based on real-time information. It makes for better decision-making and improved customer experiences across a multitude of industries.

The proliferation of smart cameras and new use cases for those cameras connected to the edge is unlikely to diminish. As AI is becoming smarter and new camera architectures are developed, there will only be further new use cases. The global smart home security camera market alone is expected to grow at a compound annual growth rate of 15.7% between 2019 and 2027, to reach $11.9 billion by 2027. Edge AI Video Analytics is the next frontier for smart cameras. By connecting to the edge, they’ll provide new applications and drive the development of even more smart cameras in the future. With Edge AI Video Analytics, we can use specialized smart cameras to enrich the products we use in our daily lives, work, and leisure.

Let’s make smart cameras with new features, like AI cameras for edge analytics and intelligent surveillance.

For anyone who wants to jump on the opportunity train of this explosive use of intelligent cameras, the easiest way to get in is probably to partner with an established player who is well-connected throughout the ecosystem. ViNotion is just that kind of partner. We not only offer our clients a comprehensive range of intelligent video interpretation technology and smart camera solutions. Our top-rated engineers and developers are dedicated to bringing advanced AI technology to benefit our society with relevant and innovative new products and services. We have also established broad partnerships and verified collaborations with system integrators, leading research and development institutes such as Eindhoven University of Technology (TU/e), and TNO. As Edge Video AI experts, we are committed to providing high-class video solutions that harness the power of Artificial Intelligence. For over 15 years, we have been silent trailblazers behind the revolution of intelligent video analytics. We are proud that today, ViNotion stands for high added-value and exceptional technology driven by Video AI.

ViNotion provides intelligent video analytics solutions that add Artificial Intelligence to cameras. Video analytics can be executed at the edge (in-camera), on on-site servers, or in the cloud. Typically, we extract only relevant objects and motion in a scene and filter out noise such as light changes, weather events, or other irrelevant information from our analytical process. Our data output is 100% privacy proof as we send out meta-data only. ViNotion is a leader in Intelligent Video Analytics technology.

Not only do we offer product designers and manufacturers various solutions that can be deployed out-of-the-box with little effort, but we also provide solutions that allow existing and even in-service regular cameras to gain artificial intelligence video intelligence features. Scaling AI video analytics technology for OEMs and new industries is part of ViNotion’s growth strategy. To this end, we work under partner development agreements. As a risk-bearing development partner, we provide our expertise, experience, and IP to accelerate the development of a new generation of innovative white-label products and devices. Applications can be found in smart cities, in healthcare, for leisure and sports, or for traffic and transportation, with many more industries and application areas to come.

It doesn’t end here. Just imagine where camera technology is heading when autonomous cars and smart refrigerators will soon be the new norm. Think about image analysis systems in your clothes, on your skin or to protect your pets. It could all be possible. There certainly seems to be no stopping AI from infiltrating our lives. Edge AI tools are getting better and more powerful, ringing in a new era for intelligent video analytics. With rising demand for smart cameras and Edge Video AI, we are all set to help product makers create relevant and exciting camera-integrated products that people need and want. Quick, with little investment in R & D and shared development risk.

Let us help you on your way to AI Vision Product Development.

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Curious about how you can get off to a favorable start and fast-track the development of innovative Artificial Intelligence video products?

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