As an industry where artificial intelligence has already had market space, the security industry has a clearer understanding and more urgent demand for the development of artificial intelligence. Artificial intelligence is promoting the third technology in the security industry after high-definition and networking. change.
In the context of the rapid development of artificial intelligence, the security industry has begun a new intelligent journey around AI. In this journey, how is the development progress of intelligent security?
Edge computing refers to the edge of the network close to the source of things or data On the other hand, an open platform that integrates the core capabilities of network, computing, storage, and applications provides edge intelligence services nearby to meet the key needs of industry digitalization in terms of agile connection, real-time business, data optimization, application intelligence, security, and privacy protection. In one sentence, edge computing can be understood as referring to computing procedures completed at the edge close to the data source.
With the continuous advancement of technology, the concept of "edge intelligence" emerged as the times require. It proposes a new model: allowing every edge device in the Internet of Things to have data collection, analysis and calculation, communication, and important functions. of intelligence. The new intelligent edge computing also takes advantage of the capabilities of cloud computing. It uses the cloud to securely configure, deploy and manage edge devices on a large scale, and can allocate intelligent capabilities according to device types and scenarios, so that intelligence can be integrated between the cloud and the edge. Flow between spaces and get the best of both worlds.
Edge intelligence has become a general trend. With the advent of the Internet of Everything era, the amount of picture and video data generated by front-end equipment in the field of computer vision is huge. If all of it is gathered into the cloud computing data center for intelligent analysis, it will bring unlimited bandwidth requirements and real-time requirements for communication. pressure. This requires providing edge intelligence services nearby and gradually migrating artificial intelligence computing power or inference capabilities from the cloud to the edge, which will help relieve the pressure on transmission links.
As a natural training ground and application field for artificial intelligence technology, the security industry has an urgent need for the implementation of artificial intelligence. In recent years, with the emergence of "brains" such as "city brain", "traffic brain", and "police brain", artificial intelligence deep learning technology combined with multi-dimensional perception has promoted the further development of AI-City.
The main research areas of deep learning are in speech recognition and vision, and applying deep learning to various directions can make different technological innovations in different fields. For the security industry that has mastered many video image resources, the combination of deep learning and security has a relatively high degree of compatibility, that is, the analysis of images and videos, including: image analysis; face recognition; word processing.
Deep learning in the security industry mainly focuses on four major areas: volume analysis, vehicle analysis, behavior analysis, and image analysis. With breakthroughs in deep learning algorithms, intelligent analysis technologies such as target recognition, object detection, scene segmentation, and character and vehicle attribute analysis have all made breakthrough progress.
In the security industry, the chip can be said to run through the whole process, from the front end to the back end , from transmission, recording to storage, security without the "core" is bound to be incomplete.
The field of security video surveillance has massive data, which can provide enough scenarios for deep learning training; in addition, in recent years, the development of intelligent algorithms has relied on massive big data to achieve important breakthroughs in speech recognition and vision. , presenting faster iterations. The implementation of artificial intelligence in the security field requires processing chips with sufficiently powerful computing capabilities. However, at the chip level, there is no artificial intelligence security application chip that fully meets actual needs.
Although artificial intelligence has completed some bluestone bridges that humans cannot do, the large-scale application of artificial intelligence has not yet come, and human intervention is needed to distinguish Differences between nearly similar objects.
Judging from actual cases, when a video of a single scene is extracted, by searching for pictures, the related pictures can be quickly revealed, and based on this, the trajectory of the criminal suspect can be discovered, and finally However, experts frankly pointed out that this process relies on artificial intelligence algorithms and is difficult to put aside human intervention, and it is still inseparable from the analysis and judgment of video criminal investigators.
Conclusion: Nowadays, the security industry has entered the era of data explosion. Faced with the explosive growth of data volume, traditional intelligent algorithms can no longer meet the needs of deep data value mining. . The deepening and deepening of artificial intelligence research has brought more changes to the security industry than imagined, and there are more and more application scenarios where it can play a role.
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