With the changes in the economic environment, the political environment, and the social environment, the demand for security is increasing in various industries. At the same time, higher requirements are placed on the application, flexibility, and humanization of security technologies. Traditional security technologies Limitations have become increasingly prominent. Against this backdrop, the development trend of artificial intelligence has roughly the following aspects:
1. Front-end intelligence: The promotion of perceptual cameras should be a general direction. If the video surveillance can identify the content of the surveillance image through machine vision and intelligent analysis, and through the background of cloud computing and big data analysis to make thoughts and judgments, and act on this basis, we can truly Let video surveillance take the place of humans to observe the world. To do this, we must have cameras that are capable of sensing. Because only the front-end camera has the perception recognition function, we can carry out the large-scale deployment and application of intelligent analysis. It is possible to turn video into data that can be used. It can be said that perceptual cameras are the basis for intelligent analysis of economics and large-scale deployment, and they are also the key to the application of smart data in big cities. If we really embrace the era of big data, perceptual cameras are undoubtedly the cornerstone of video surveillance.
2. Deep learning: Research and application of various self-learning and adaptive algorithms. Subsequent smart analysis products should have strong self-learning and adaptive capabilities. It can automatically learn and filter according to different complex environments, and can automatically filter some interference targets in the video. In order to achieve improved accuracy and reduce the complexity of debugging purposes. For example, Kodak Falcon's bayonet analysis system integrates industry-leading face detection algorithms, face tracking algorithms, personnel tracking algorithms, face quality scoring algorithms, face recognition algorithms, human attribute analysis algorithms, and human goals. Search algorithm. It can realize the face capture, identification and attribute feature information extraction of people's access to the city's major places, establish a city's massive human face feature database, and use the actual application of police as the core to innovate actual combat techniques. By docking the public security information resources database, it can provide early warning and real-time warning to terrorist-related, security-related, and criminal elements, real-time dynamics, track analysis and tracking of suspects, and quickly lock the trajectory of suspects; Personnel conduct rapid identification to provide key clues for case detection. Through the construction and application of this system, the leap-forward development of public security work in the era of big data will be realized, and work efficiency will be further improved, resource costs will be saved, and the period for solving cases will be shortened.
3. Big Data Mining: The rapid development of video data mining applications. With the rapid development of video analysis technology, the amount of video data is also very large. How to make video analysis technology play a role in big data has also become a focus of attention. Using various algorithms to calculate, a large number of things in different properties of video data are used for searching, labeling, identification, and other applications to achieve fast search and retrieval of large amounts of data. Greatly reduce labor costs and increase efficiency. There are even aspects that make it impossible for some human to complete the task. Such as: face, personnel database search, identity card library duplicate personnel to find, through the semantic description from the video to find wear a certain clothes, a color of the vehicle to find, license plate to find, in order to map search, video associations and other applications. Targeting large-scale video workloads in the construction of safe cities, and different video scenes, and difficulty in quickly finding targets, Keda has introduced a structured analysis system, which is designed specifically for mass images and videos. The application system for analysis is applicable to various media sources with multiple scenarios and large differences. Supports real-time analysis of accessing multiple types of front-ends; supports secondary analysis of offline image and video for target detection, attribute analysis, and feature extraction; supports distributed deployment and expansion.
In the security industry, the most used artificial intelligence algorithms are still in the field of video images, because the products of traditional security companies are related to video images. However, for public security and other business applications, video images are only a small part, and public security applications also require network information, communication information, social information, and so on. In the future, the security industry will also need to base on video and image information to open up a variety of heterogeneous information. On the basis of massive heterogeneous information, it will give full play to the advantages of various artificial intelligence algorithms such as machine learning, data analysis and mining, and create security for the security industry. More value.
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