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In recent years, the concept of data-driven artificial intelligence learning and recognition technology has also become quite popular in the security industry. What is its connection with security? How to apply it in security monitoring? What are the most common applications of this type of AI currently?
Security AI artificial intelligence combined with data collection
Since the rise of road monitoring systems worldwide, urban monitoring construction in various countries is about to enter a stage of expansion and structural change. Under this demand transformation, security monitoring systems will require more diversified and AI based overall solutions. Modern public safety is no longer limited to unlimited expansion of image surveillance coverage density, breadth, and pursuit of ultra-high definition resolution. Instead, through these artificial intelligence means and tools, it takes the traditional security era further and shifts towards an AI based security era that focuses on data collection, application, and management.
The construction of global urban road monitoring is rapidly developing, and various camera monitoring devices can be seen everywhere on streets and intersections in various countries, providing convenience and immediacy for urban public safety and security reconnaissance work. But with the significant increase in the number of monitoring devices and the continuous improvement of image resolution, the data volume of images and pictures collected by public safety has shown an exponential growth. In addition, the improvement of image resolution has also raised the threshold for the processing capacity and utilization rate of servers. Therefore, security image monitoring faces enormous challenges in technology such as image retrieval, access control data, data storage, and computation.
Promote the future of big data in security
Driven by innovation in the AI analysis market, people are mining valuable data information in image surveillance, which is not limited to basic information about people, things, and objects. At the same time, it also requires the strong research and development capabilities of manufacturers to continuously supplement key information collected from security big data. This not only brings more valuable data to the final big data platform, but also provides a continuous source of product development momentum for deep AI in the application of security industry data.
