Edge Video Analytics and 5G Cloud Monetization
The 5G telecommunications industry stands to save money in the long run by developing a programmable software framework for telecommunication operations. To many of us in the business, the most exciting aspect of the impending convergence of telecommunications, the cloud, and edge infrastructures is the possibility it may lead to new technologies and revenue streams for both the telecommunications industry and the cloud ecosystem.
Since the advent of 4G networks, video has become the most prevalent kind of online traffic, so that's where our attention is going to be focused in this article. In addition to the already expected growth in video traffic, technology for 5G cloud monetization will also usher in a slew of innovative solutions for sectors as diverse as retail, manufacturing, and healthcare, all of which will incorporate deep learning and artificial intelligence (AI) for video analytics. Operators may expand their service offerings and generate additional revenue streams as video analytics and edge computing continue their symbiotic development.
To improve response times and save bandwidth, edge computing makes it possible to move AI tasks for video analytics from the cloud to the edge. As a result, there has been a significant uptick in research into Edge AI technologies for video analytics.
AI-assisted video devices have benefited greatly from the increased processing efficiency made possible by Edge AI technology. Edge AI's low bandwidth consumption, improved latency, and reduced power constraints have a direct effect on the quantity and quality of tasks.
Edge AI tools are already paving the way for a new era of intelligent video analytics, and their capabilities and applications are only expected to grow as they progress.
What Are Edge AI and Video Analytics?
The term "Edge AI" refers to the idea of offloading data processing from the cloud to a location as close as possible to the machine in question. In comparison, video analytics refers to extracting information from videos.
Both academic institutions and businesses have taken a keen interest in video analytics over the past few years. Most notably, video analytics has automated tasks that were previously performed solely by humans.
Video analytics has many applications, including retail store optimization through customer foot traffic analysis, traffic monitoring and real-time alerts, and the completion of tasks like smart parking and Face Recognition.
When AI at the edge is combined with video analytics, you get a reliable, locally-based source of AI-enhanced video analysis. For instance, a business can use the combined capabilities at the network's edge to operate a video program that can automatically identify spatial and temporal events within videos.
The Role of Edge AI in Video Analytics
The importance and complexity of IT architecture has been brought to the forefront by the increasing reliance on data by businesses. Businesses in the modern era are increasingly data-centric. Because of the proliferation of IoT and big data, businesses now have to deal with a previously unimaginable increase in the number of data touch points required by any application. The amount of information that must be processed, stored and managed grows as more and more endpoints contribute data. This, along with the development of AI programs and the widespread availability of high-definition cameras, has led to an explosion in the amount of data gathered by video analytics. Bandwidth shortages and delays caused by processing all this data in the cloud can be problematic in some scenarios, such as those involving security.
It is possible to reap a host of benefits by putting video analytics to use at the network's edge. It can, for instance, deal with compliance rules and regulations, prevent bandwidth issues, and speed up data access. Video analytics supported by Edge AI can provide faster insights directly, allowing for crucial decisions to be made in real time.
Video analytics can be run from remote clouds or local servers. Edge computing and Edge AI have only recently been able to store and run commands deduced by video analytics. There has been a significant increase in the sales of AI-enabled and intelligent cameras. It is expected that most IP cameras shipped will have artificial intelligence capabilities, opening the door for much of this AI work to be performed at the Edge. As AI becomes more widely available, it is likely that more and more cameras will include intelligent video analytics at the Edge because of the benefits it provides.
The Convergence of 5G, Video Analytics, and Edge Computing
With the onset of 5G, telecom companies have begun investing heavily in their network infrastructure, allocating the bulk of their bandwidth to support video traffic. Video analytics is an ideal opportunity for 5G operators to host on their edge computing servers, and edge computing is the driving force behind the coming together of the infrastructures.
Current Technology Could Be A Moneymaker For 5G Providers
We can foresee several viable scenarios where 5G cloud monetization can help operators profit from deploying video analytics services. Think about ways to keep the roads safe and track down accidents before they happen in smart cities. An example use case might involve incorporating real-time video analysis into self-driving cars to detect automobiles, pedestrians, and cyclists.
Also, think about the smart businesses of today, where video analytics and mixed reality are a natural part of private 5G network solutions that help improve end-to-end user experiences. Other scenarios include the management of automated equipment in interconnected factories, customer satisfaction in stores and restaurants, and the flow of crowds in sports venues.
5G operators, along with System Integrators, can leverage video analytics services to devise innovative responses to all these scenarios. For example, operators can facilitate a vastly improved gaming experience for the next generation of global game streaming services by leveraging the potential of low-latency, high-bandwidth 5G networks and live video analytics on edge devices.
There are huge advantages that come about when video analytics are deployed on the Edge. The future of security, from multinational corporations to mom-and-pop shops down the street, is in smarter, more efficient hands than ever before thanks to the combination of Edge AI with video analytics. Edge AI video analytics can also be useful for monitoring traffic, analyzing sales data, performing quality assurance checks, and performing other recognition-related tasks.
Knowledge Base Team
Knowledge Base Team
Knowledge Base Team
Knowledge Base Team
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