Edge AI Solutions for Physical Security
Driven by the proliferation of IoT devices, advances in machine learning and the overall progress of technology, edge computing and edge AI are now undergoing rapid development. It is predicted that by 2024 the edge AI software market alone will grow from $346.5 million to about $1.1 billion, while the total global edge computing market will increase by 37.4 percent per year and will be worth $43.4 billion by 2027.
The potential for edge intelligent solutions is huge. Edge AI applications vary from surveillance cameras with video analytics and smartwatches to autonomous vehicles and smart cities. Numerous enterprises across industries are beginning to show a significant adoption of edge devices with intelligent analysis to drive new levels of performance and productivity.
Traditionally, data gathered at the edge is transmitted to the main server or a cloud for further processing and analysis. This process takes time and can be costly. Deploying edge AI for physical security helps to curb these issues as well as meet the need for real-time predictions. In a critical situation, the ability to detect, analyze and immediately respond to an emerging threat is of vital importance as it helps mitigate risks and prevent escalation of potentially dangerous scenarios. Let’s explore how edge AI works and what makes it beneficial for organizations.
What is edge AI?
Simply stated, edge AI is a combination of edge computing with artificial intelligence. Edge computing implies that processing occurs at the local level right where the event takes place. With AI it becomes possible to use machine learning algorithms for real-time analysis of the data generated by edge devices.
Devices don’t need to be connected either to the Internet or the cloud as they have enough computing power to produce all the necessary analytics independently. Decision-making is a matter of milliseconds due to the fact that data is processed locally or in nearby edge data centers and doesn’t have to travel far.
With the improvement of hardware capacity, edge AI solutions now can gather and store a much larger amount of data on edge devices and perform advanced tasks like object, intrusion, or anomaly detection. They can also autonomously improve their own performance on a given task by learning from data.
What are the benefits of edge AI?
● Reduced costs
The most significant benefit of edge AI is saving costs. Technically, edge processing and analyzing large amounts of data is cheaper compared with cloud computing due to savings on the cost of bandwidth. Also, you don’t have to buy a lot of capacity from a cloud service if you want fast response times when analyzing constant data streams or large amounts of data. Though edge AI requires local computing power and investing in hardware, edge AI solutions are becoming more and more available as sensors, cameras, GPU processors, and other hardware are constantly becoming cheaper. The costs can also be saved by making devices more energy efficient.
Leveraging edge computing capabilities gives companies flexibility and allows them to scale their data and computing needs more efficiently. As companies grow, they cannot always anticipate their IT infrastructure needs. So, establishing centralized, private data centers might be premature and too costly to build, maintain and replace when the company grows again. In this respect, devices situated nearer to end-users and using AI computing, storage, and analytics capabilities are a more viable option for organizations that want to expand their edge network and scale their operations quickly and cost-effectively.
● Real-time analytics
With a growing need for real-time analytics, edge AI solutions are the right option. They are internet-independent and can analyze streaming data and produce sophisticated analytics in a fraction of a second. That’s a good advantage for sites where the internet connection is unreliable and can cause data transfer disruptions.
Another significant benefit of edge AI is its ability to increase network performance by reducing latency. As the amount of data gathered from sensors or video surveillance cameras is often vast, streaming all this volume to a cloud or data center for processing takes time. Also, there may not be enough available bandwidth to transmit all the raw information. This is often not a problem but in critical situations, when the response time requirement is high, it makes the system inefficient. Transferring data processing and analytics to the location where the data is collected solves this problem as it reduces the need for data transfer and essentially eliminates latency.
● Information security and privacy
When it comes to security issues, privacy and the security of information are big concerns. Traditional cloud computing architecture is inherently centralized, which makes it potentially vulnerable to attacks and power outages. With edge computing, ensuring data privacy and security is much easier as there is no need to transmit and store data in a cloud environment. It is processed in real-time and might only exist for a blink of an eye before it disappears. Thus, operating locally in a closed network, Edge AI leaves fewer opportunities for online attacks and stealing sensitive information.
● Easy maintenance
Edge technology devices are self-contained and easy to set up and use. They do not require specialized maintenance by data scientists or AI developers. The graphic data flows are automatically delivered for monitoring and displayed for the user with help of highly graphical interfaces or dashboards.
Being independent of the cloud, edge technology shows improved reliability. Edge AI devices are less vulnerable to operational risks and maintain their ability to function even if the centralized cloud computer or cluster fails.
The use cases for physical security
Edge AI brings benefits to many industry sectors. It is also applicable to surveillance systems especially when considering home and small business security. Those who have a home or small business camera network can count on edge AI devices to augment their security infrastructure in a cost-effective way. Generally, raw information received from security cameras is continuously streamed to a cloud server for further processing and analysis. This causes a big volume of video footage and consequently a heavy load on the cloud server. With edge AI, the bandwidth use can be reduced as machine learning enables analysis at the local level. As a result, the server can communicate with a higher number of cameras to improve situational awareness at their site.
Connected to cameras, edge AI solution monitors surroundings in real-time, helps accurately identify people, animals, and cars entering or leaving the property, detects threats, and alerts the owner to potential danger. With false alarm filtering technology, the system automatically filters out non-actionable events like foliage movements, shadow changes, or animal movement.
Integrated with anomalous behavior solutions, edge AI devices can be deployed to monitor safety conditions within premises to see whether the employees comply with prespecified regulations. An edge AI inference system combined with IP cameras monitors their movements, identifies abnormal behavior such as lingering or working without protective equipment, and sends notifications in case of violations. The system can also be used for mask checks to manage the spread of COVID-19. Another option is to identify starting fires by detecting early signs of smoke.
Edge AI can serve to improve parking lot operation by license plate recognition. Traditional rule-based license plate recognition can fail due to obscure camera angles or weak ambient lighting. AI technology helps solve these problems. The AI system precisely scans and analyzes vehicle plates and provides automatic parking lot access. These quickly conduct accurate license plate recognition that improves parking experiences and lot efficiency.
Edge AI data analytics is about to take a huge leap. Though it is not going to replace a traditional way of data management and analysis performed in the cloud or data centers, it will definitely open up opportunities for businesses to optimize their networks and drive new levels of performance and productivity. Additionally, it will ensure higher levels of security in terms of data privacy and reduce costs for organizations.
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