In recent years, the competition in the cloud market has been intense. Whether it is foreign tech giants like Amazon, Microsoft, and Google, or domestic companies like BAT, they have all invested significant resources into developing cloud computing services, and migrating enterprises to the cloud has gradually become a market consensus. However, with the geometric increase in internet data, traditional cloud computing architectures are slowing down. A well-known venture capital research organization, CB Insights, has stated that cloud computing is no longer sufficient to process and analyze the data generated or soon to be generated by IoT devices, connected vehicles, and other digital platforms in real-time. Against this backdrop, a new computing paradigm has emerged: edge computing.
Unlike cloud computing, which relies on multiple data centers, edge computing refers to computation performed at the data source, characterized by low latency, security, and high flexibility. IT research and market analysis firm Gartner believes that edge computing will bridge the “last mile” of artificial intelligence.
With its secure and efficient characteristics, edge computing has garnered significant attention from many enterprises and industries. As a typical application of edge computing, the field of security video surveillance should not be underestimated. If cloud computing is considered the era of video surveillance 1.0, then edge computing represents the era of 2.0.
Improving the Processing and Response Speed of Surveillance Terminals
Video surveillance has high demands for computing power and its costs. With the development of image recognition and hardware technology, the conditions for completing intelligent security at video surveillance terminals are becoming increasingly mature, compensating for the issues of delayed responses and high power consumption associated with cloud computing. The security industry’s needs for real-time operations, safety, and privacy protection are thus being met, leading to widespread application.
Compared to traditional video surveillance, the most significant change with edge computing + video surveillance is the transformation from passive monitoring to active analysis and early warning, thereby addressing the need for human intervention in processing massive amounts of surveillance data. Essentially, edge computing preprocesses video images, removing redundant information, and shifts some or all video analysis to the edge, thereby reducing the demand for cloud center computing, storage, and network bandwidth, while increasing the speed of video analysis. Additionally, edge preprocessing can employ software optimization and hardware acceleration methods to enhance the efficiency of video image analysis.
For example, with facial recognition cameras, enhancing the computational processing capabilities of the camera terminals allows the facial recognition function to no longer rely on cloud servers, completing identification directly on local devices, thus avoiding the time-consuming process of uploading images and saving bandwidth resources.
Optimizing Data Storage Mechanisms
The storage phase directly impacts the intelligence level of the surveillance system, especially in the current context of deep learning technology development. It has become increasingly important to establish a behavior-aware elastic storage mechanism for video surveillance data, enabling the processing of behavior-aware data in surveillance scenarios.
Edge computing provides a platform with preprocessing capabilities for video surveillance systems, allowing for real-time extraction and analysis of behavioral features in videos, and adjusting video data based on these behavioral features. This not only reduces ineffective video storage and lowers storage space requirements but also maximizes the storage of “in-situ” evidence-type video data, enhancing the credibility of evidence information and improving the utilization of video data storage space.
Conclusion: Edge computing is an important part of the future of video surveillance. By preprocessing video images and reducing the storage burden on cloud centers, it further enhances the speed of video analysis. It is worth mentioning that the ultimate goal of edge computing is not to replace the cloud but to extend the cloud’s margins through a distributed architecture, bringing it closer to user networks to meet the higher demands for network latency and bandwidth in emerging applications.