Introduction to Edge Computing for Video Analytics
In this digital era, where everything is recorded and analyzed. There are about 250 million CCTV cameras around the world. The city with the most cameras has 150 cameras per 1,000 people. The number of cameras is constantly increasing, and more and more cities are investing in video surveillance. Employing technology for real-time analysis is becoming paramount. One estimate (from IDC) forecasts that the amount of data generated worldwide destined to be used for analysis will increase 50-fold by 2025.
Brings data storage and processing close to where data is being generated or gathered instead of at the servers located at thousands of miles. Click to explore about our, Applications of Edge Computing
The problem with Video Analytics
Deploying the most reliable storage solution is crucial, given video's increasingly important role in security and operational needs. Because AI systems significantly rely on data and video fidelity, businesses cannot afford to lose either. For AI applications to "learn" and increase their capacity to do predictive analysis, they must store extra data for long periods. The storage infrastructure and capacity must be higher to achieve performance optimization when more AI capability is incorporated into operations.
To increase productivity and reduce downtime, every storage device utilized for these sophisticated applications must be as dependable as possible. Unfortunately, relying solely on cloud data centers can cause problems for data-intensive applications. These systems frequently have latency problems, which can slow the delivery of real-time, mission-critical information, depending primarily on the Internet connection and the distance between the cameras and their server.
The solution
Many advantages can be achieved by implementing edge computing devices, including lowering data access latency, eliminating bandwidth problems, and managing data compliance requirements. The result is a reduction in maintenance calls and an increase in overall customer satisfaction. The result may be an increase in profitability. The ability to make crucial decisions in real-time is made possible by edge devices driven by AI-enabled drives, which give faster insights directly on the spot.
What is Edge Computing?
It is a distributed information technology (IT) architecture in which client data is processed as near the original sources as possible at the network's edge.
In its simplest form, edge computing involves moving a portion of the storage and processing capacity out of the main data center and toward the data source. Instead of sending unprocessed data to a centralized data center for processing and analysis, that work is now done where the data is generated, whether on the floor of a factory, in a retail establishment, at a large utility, or throughout a smart city. The only output of the computer effort at the edge delivered back to the primary data center for analysis and other human interactions are real-time business insights, equipment repair projections, or other actionable results.
Why Edge Computing for Video Analytics?
Artificial intelligence (AI) systems are increasingly used to analyze photos and videos, identify and recognize objects and people, and derive useful information from what they perceive.
Why is cloud computing not sufficient for Video Analytics?
AI-powered video analysis needs an enormous amount of computing power. This explains why cloud computing was used primarily in the early days of video analysis. Unfortunately, although cloud computing has numerous benefits, there are better choices for applications where latency (speed of response) is a concern. For most mission-critical and security applications, latencies between 100 and 500 ms are unacceptable when doing cloud-based video analysis utilizing a cloud services provider.
Another concern is the possibility of several 4K or greater resolution high-definition cameras being used in modern vision systems. The quantity of bandwidth needed and the related costs would be high if all of this data were to be sent to the cloud for analysis.
Security
Security is another problem. You risk having your data compromised by hackers whenever you transfer data outside of the building. The need for a dependable Internet connection is a different and crucial factor. Losing your Internet connection while using a commercial program is frustrating. In contrast, if your AI-enabled video analysis is being done in the cloud for industrial and transportation applications, the loss of Internet access might cause harm or even death.
Edge computing video analytics
The good news is that edge video analysis (EVA), or on-site real-time video analysis, is now possible because of technological advancements and AI algorithms. Parallel processing benefits many AI algorithms, including those that use matrix operations. Adding graphics processing units (GPUs), which include thousands of tiny processors, each with its local memory, can significantly increase the capabilities of today's potent microprocessor units (MPUs). However, in this instance, the GPUs are being used to execute the video analysis AI algorithms in massively parallel rather than manipulating visual data for display.
Allows data generated by IoT to be processed near its source rather than sending the data to a great distance to data centers. Click to explore about our, Artificial Intelligence in Edge Computing
What are its Use Cases?
Edge computing for Video Analytics is used in various fields, and several use cases exist. Some will be discussed below:
Autonomous Vehicles
Autonomous vehicles are one of the best examples of it in video analytics. Suppose you have a driverless car, and the sensor sends the data to the cloud, but the latency will be high due to poor connectivity. With edge computing, the sensors will send the data to the edge device and respond quickly without any delay so that it can save lives.
Another use case is, Autonomous platooning of trucks. A group of truck traveling closes behind one another in convoy, saving fuel costs and decreasing congestion. With it will be possible to remove the need for drivers in all trucks except the front one because the trucks will be able to communicate with each other with ultra-low latency.
Surveillance and Security
Government and corporate security constitute a large volume of video and camera surveillance. Many countries like the USA, China, and the UK each use millions of cameras to monitor activities on public sidewalks and roads. Increasingly, even police officers are wearing body cams. This big data can be analyzed and monitored in real time.
Track store foot traffic in real-time
In any store or supermarket, having a count of customers and how customers move around the store can benefit stores. These types of insight can help stores optimize product placement, fine-tune staff levels, and even safety.
Avoid lost sales from product stockouts
With the help of Edge Video analytics, stores can avoid lost sales. Product stockouts are one of the retail organizations' most significant drivers of lost sales. Product intelligence-based edge video analytic solutions can help mitigate this problem by automatically detecting when a given product goes out of stock on a shelf and notifying employees to restock the product.
Face recognition in the workplace
Face recognition systems can be implemented at the workplace for attendance, identifying the workers, and matching faces from the database. It can also identify the unknown person and notify the management if any unknown person is found where he should not be.
Here are a few more examples:
- Keep customer queues short
- Real-time traffic monitoring and dynamic situational adaptation
- Gain insights into product popularity
- Recognition-based security systems
- Facility inspection systems
Conclusion
Combining Video analytics with it becomes more powerful. It allows analysis in real time with low latency. In some applications, delaying response could be a matter of life and death, but with edge computing, it can be avoided. Its video analytics can be used in several applications like traffic monitoring, retail analytics, quality control, and recognition tasks.
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