News
2025.01.15
With the widespread application of artificial intelligence, Edge AI has become the preferred solution in the field of security monitoring. Edge computing focuses on real-time data processing near the data source (such as surveillance cameras), which not only reduces transmission delays but also significantly enhances system responsiveness.
As the demand for security monitoring continues to rise, companies require AI technology to improve monitoring efficiency, reduce labor costs, and respond quickly to potential threats.
Source:freepik.com
Spark will delve into the core concepts, advantages, practical use cases, and future developments of edge computing and AI monitoring technology, helping businesses understand why Edge AI solutions have become a key choice in modern security monitoring.
What is Edge AI technology?
Edge AI refers to the combination of edge computing and artificial intelligence, which involves deploying AI algorithms and models directly on local edge devices to execute machine learning tasks without relying on cloud systems. This includes devices such as sensors or Internet of Things (IoT) devices, enabling rapid data processing and analysis to reduce latency and bandwidth requirements, thereby enhancing application performance and response speed.
Edge computing allows data to be stored close to the device, while AI algorithms can process this data in real-time at the network's edge, regardless of internet connectivity. This facilitates data processing within milliseconds, providing real-time feedback. Technologies such as self-driving cars, wearable devices, surveillance cameras, and smart appliances fully leverage the capabilities of Edge AI, allowing them to deliver crucial information to users at critical moments.
Edge AI vs. cloud Computing
Traditional cloud computing typically relies on centralized servers, where data is transmitted over the internet to the cloud for processing, which can lead to delays in certain applications. In contrast, edge computing moves computation and storage closer to the data source, significantly reducing latency and bandwidth demands.
Edge AI vs. Distributed AI
Edge AI processes data mainly near the data source, such as on IoT devices or edge servers, which can minimize data transmission delays and enhance real-time response capabilities. Distributed AI distributes data and computational resources across multiple locations but is not necessarily near the data source. For example, it might involve multiple servers or cloud resources to run AI algorithms and process data. Integrating Edge AI with Distributed AI can further enhance efficiency by allowing preliminary data analysis on edge devices, selecting important data to send to the cloud for model retraining, and consolidating information from multiple devices. This is applicable in areas such as smart manufacturing, smart cities, and healthcare. For more information on integration solutions, please contact us.
Source:freepik.com
Main advantages of Edge AI
1. Real-time processing: Since data is processed at the source, image recognition can be performed at the monitoring site without the need to transmit to cloud servers, allowing the system to respond quickly.
2. Improved efficiency and accuracy: Through algorithms, Edge AI can accurately identify individuals, objects, and unusual behaviors, enhancing the accuracy and efficiency of event detection.
3. Reduced latency: By processing data at the edge, Edge AI can decrease the amount of data transmitted to the cloud, which not only saves bandwidth but also lowers the cost of cloud services.
4. Bandwidth savings: Processing data at the edge reduces the volume of data sent to the cloud, thereby saving network bandwidth.
5. Enhanced privacy and security: Since image processing occurs locally, Edge AI can ensure that sensitive data does not need to be uploaded to the cloud for analysis, reducing the risk of data leaks and ensuring user privacy.
6. Diverse application scenarios: Edge AI technology can be applied in various image processing scenarios, including smart monitoring, autonomous driving, and industrial automation, enabling real-time object detection and tracking and supporting a wide range of complex image analysis tasks.
Applications of Edge AI in Recognition and Analysis
Why Enterprises Choose Edge AI Monitoring Solutions
Cost-effectiveness: Reducing reliance on human oversight
Through AI monitoring, enterprises can decrease their dependence on human supervision, thereby saving costs and allocating resources more effectively.
Agile response: Real-time data processing and quick alerts
Edge computing enables enterprises to process monitoring data in real-time, allowing for immediate alerts and enhancing emergency response capabilities.
Enhancing customer and employee trust and satisfaction
An effective security monitoring system can increase customer and employee trust in the enterprise, thereby boosting their overall satisfaction with the company.
Application Case of Edge AI in Monitoring
Hsinchu County Road Monitoring Case
Hsinchu County, has a superior geographic location and a well-developed transportation network, with its main roads and intersections forming a busy traffic hub that attracts a large volume of traffic daily. To address this challenge, Spark D-Wave Technology assisted the Hsinchu County Police Department by implementing the AI VMS Mini at the Xingxue Street intersection, upgrading the existing camera system to support real-time uploading of license plate information to the police system. This helps the police keep track of road traffic conditions in real-time, enhancing the efficiency of road monitoring and public safety maintenance.
Integration of Edge AI and Image Recognition Technology
With the rapid development of 5G and Internet of Things (IoT) technologies, the integration of edge computing and AI image recognition technology will become more profound, bringing new possibilities to the future of the security surveillance industry. We are excited about the flourishing of safer and more efficient monitoring solutions. Spark’s AI monitoring solutions offer advantages across multiple dimensions, including enhanced security, reduced costs, and improved customer trust. If you would like to implement or learn more, please feel free to contact us.
With the widespread application of artificial intelligence, Edge AI has become the preferred solution in the field of security monitoring. Edge computing focuses on real-time data processing near the data source (such as surveillance cameras), which not only reduces transmission delays but also significantly enhances system responsiveness.
As the demand for security monitoring continues to rise, companies require AI technology to improve monitoring efficiency, reduce labor costs, and respond quickly to potential threats.
Source:freepik.com
Spark will delve into the core concepts, advantages, practical use cases, and future developments of edge computing and AI monitoring technology, helping businesses understand why Edge AI solutions have become a key choice in modern security monitoring.
What is Edge AI technology?
Edge AI refers to the combination of edge computing and artificial intelligence, which involves deploying AI algorithms and models directly on local edge devices to execute machine learning tasks without relying on cloud systems. This includes devices such as sensors or Internet of Things (IoT) devices, enabling rapid data processing and analysis to reduce latency and bandwidth requirements, thereby enhancing application performance and response speed.
Edge computing allows data to be stored close to the device, while AI algorithms can process this data in real-time at the network's edge, regardless of internet connectivity. This facilitates data processing within milliseconds, providing real-time feedback. Technologies such as self-driving cars, wearable devices, surveillance cameras, and smart appliances fully leverage the capabilities of Edge AI, allowing them to deliver crucial information to users at critical moments.
Edge AI vs. cloud Computing
Traditional cloud computing typically relies on centralized servers, where data is transmitted over the internet to the cloud for processing, which can lead to delays in certain applications. In contrast, edge computing moves computation and storage closer to the data source, significantly reducing latency and bandwidth demands.
Edge AI vs. Distributed AI
Edge AI processes data mainly near the data source, such as on IoT devices or edge servers, which can minimize data transmission delays and enhance real-time response capabilities. Distributed AI distributes data and computational resources across multiple locations but is not necessarily near the data source. For example, it might involve multiple servers or cloud resources to run AI algorithms and process data. Integrating Edge AI with Distributed AI can further enhance efficiency by allowing preliminary data analysis on edge devices, selecting important data to send to the cloud for model retraining, and consolidating information from multiple devices. This is applicable in areas such as smart manufacturing, smart cities, and healthcare. For more information on integration solutions, please contact us.
Source:freepik.com
Main advantages of Edge AI
1. Real-time processing: Since data is processed at the source, image recognition can be performed at the monitoring site without the need to transmit to cloud servers, allowing the system to respond quickly.
2. Improved efficiency and accuracy: Through algorithms, Edge AI can accurately identify individuals, objects, and unusual behaviors, enhancing the accuracy and efficiency of event detection.
3. Reduced latency: By processing data at the edge, Edge AI can decrease the amount of data transmitted to the cloud, which not only saves bandwidth but also lowers the cost of cloud services.
4. Bandwidth savings: Processing data at the edge reduces the volume of data sent to the cloud, thereby saving network bandwidth.
5. Enhanced privacy and security: Since image processing occurs locally, Edge AI can ensure that sensitive data does not need to be uploaded to the cloud for analysis, reducing the risk of data leaks and ensuring user privacy.
6. Diverse application scenarios: Edge AI technology can be applied in various image processing scenarios, including smart monitoring, autonomous driving, and industrial automation, enabling real-time object detection and tracking and supporting a wide range of complex image analysis tasks.
Applications of Edge AI in Recognition and Analysis
Why Enterprises Choose Edge AI Monitoring Solutions
Cost-effectiveness: Reducing reliance on human oversight
Through AI monitoring, enterprises can decrease their dependence on human supervision, thereby saving costs and allocating resources more effectively.
Agile response: Real-time data processing and quick alerts
Edge computing enables enterprises to process monitoring data in real-time, allowing for immediate alerts and enhancing emergency response capabilities.
Enhancing customer and employee trust and satisfaction
An effective security monitoring system can increase customer and employee trust in the enterprise, thereby boosting their overall satisfaction with the company.
Application Case of Edge AI in Monitoring
Hsinchu County Road Monitoring Case
Hsinchu County, has a superior geographic location and a well-developed transportation network, with its main roads and intersections forming a busy traffic hub that attracts a large volume of traffic daily. To address this challenge, Spark D-Wave Technology assisted the Hsinchu County Police Department by implementing the AI VMS Mini at the Xingxue Street intersection, upgrading the existing camera system to support real-time uploading of license plate information to the police system. This helps the police keep track of road traffic conditions in real-time, enhancing the efficiency of road monitoring and public safety maintenance.
Integration of Edge AI and Image Recognition Technology
With the rapid development of 5G and Internet of Things (IoT) technologies, the integration of edge computing and AI image recognition technology will become more profound, bringing new possibilities to the future of the security surveillance industry. We are excited about the flourishing of safer and more efficient monitoring solutions. Spark’s AI monitoring solutions offer advantages across multiple dimensions, including enhanced security, reduced costs, and improved customer trust. If you would like to implement or learn more, please feel free to contact us.