In recent years, with the help of basic capabilities such as networks, data, computing, chips, and algorithms, with the continuous maturity of technologies and applications such as the Internet of Things, big data analysis, artificial intelligence, especially computer vision, video structured analysis, The introduction of artificial intelligence technologies such as video image deep learning, the in-depth mining of public security big data and social big data, the level of urban public security intelligence continues to increase. "AI + Smart Security" is becoming a hot spot for the development of the security industry.
With the continuous application of technologies such as AI and big data in the field of security. The future development trend of smart security is mainly manifested in three aspects:
(1) The combination of AI and front-end sensing devices, by giving front-end edge computing capabilities, you can front-end some intelligent analysis functions such as portrait recognition, vehicle recognition, behavior recognition, etc., which not only meets the intelligent application of urban security to multiple subdivision scenarios It is required to reduce the pressure of data transmission on the network and the dependence on the data center through the marginalization of computing and data, and improve the intelligent efficiency of the entire network.
(2) The 2030 Artificial Intelligence Life Report released by Stanford University in the United States comprehensively evaluates the development of artificial intelligence, and lists human-computer mutual compensation and enhanced intelligent collaborative systems as one of the important development trends of future AI. Human-robot collaboration Intelligent collaboration requires machine recognition capabilities based on human state models, human-machine knowledge sharing capabilities based on knowledge maps, and multi-person, multi-machine global planning capabilities based on intelligent reasoning. In the field of intelligent security, human-machine systems can play a role in scenarios such as remote emergency command, joint search and rescue at accident scenes, and operations in areas not reachable by humans, enabling active recognition of human-machine organizations and mutual coordination. For example, in a critical environment, citizens can send help signals to video surveillance systems through gestures.
(3) Security big data gathers data from multiple departments and multiple systems at different points in time. The lack of global data analysis capabilities severely limits the application of security data. In the future, letting machine learning process real-time, ultra-large-scale, full-scale, multi-source data that humans can't understand, understanding complex hidden rules from massive data, and finally being able to formulate optimal strategies from a global perspective are the key directions for the development of smart security.
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