In recent years, the security industry has also emerged a very popular concept of data-based artificial intelligence learning and recognition technology. How do they relate to security? How to apply it in security monitoring? What are the current applications of AI artificial intelligence?
Security AI artificial intelligence combined with data acquisition
Since the rise of road monitoring systems around the world, urban monitoring and construction in the world is about to enter the stage of expansion and structural change. Under this demand change, security monitoring systems will require more diversified and artificial intelligence. The modern public safety is no longer limited to the unlimited expansion of image surveillance coverage density, breadth and the pursuit of ultra-high definition resolution, but through these artificial intelligence means and tools, the traditional security era goes further and turns to focus on data. The era of artificial intelligence security for collection, application and management.
The global urban road monitoring and construction is developing rapidly. Various camera monitoring equipments can be seen everywhere in the streets and intersections of various countries, providing image convenience and immediateness for urban public safety and public security reconnaissance work. However, with the large number of monitoring devices and the increasing resolution of images, the amount of data collected by public safety images and images has increased geometrically, and the resolution of images has increased, which has led to the processing of servers. Both capacity and usage have created a higher threshold. Therefore, security image surveillance faces enormous challenges in image access, access control data, data storage, and computing.
Application technology of AI artificial intelligence and security monitoring
Faced with such challenges, how security monitoring users can quickly acquire valuable data using existing artificial intelligence technology in a large amount of data has become an important issue. The following is a brief description of several AI artificial intelligence technologies combined with security monitoring:
1. Pattern recognition technology of artificial intelligence
Usually, in the image data collected by the monitoring system, the data itself is not valuable, and it must be deeply excavated and analyzed to analyze the data patterns presented in the data to produce truly useful value. The future is the era of big data, and the pattern recognition of data materials will be highly valued.
2. Deep learning technology of artificial intelligence
This is a new field in AI artificial intelligence machine deep learning research. The motivation is to establish and simulate the neural network for human brain analysis and learning. It mimics the human brain's behavioral thinking mechanism to interpret data such as video content, sound and data. itself. In the future, the machine deep learning of AI artificial intelligence can be popular, the data itself will be the main key factor, and the image monitoring data accounts for more than 60% of the total amount of big data, that is, more than 70% of the data in the field of image monitoring. Data analysis is used for image recognition. At present, this kind of AI machine deep learning has made great progress in many fields of the security industry, including: pedestrian detection, vehicle detection, non-mobile vehicle detection, etc., and its recognition accuracy even exceeds human eye judgment.
3. Front-end recognition technology of AI artificial intelligence
Advanced product technology is the foundation for a long-term development of a high-tech enterprise. To ensure intelligent monitoring of security, the system needs to have an "image recognition" computing technology based on AI artificial intelligence to develop a series of intelligent monitoring applications. Equipment, so front-end recognition technology has become the third essential technology of AI artificial intelligence.
After a general introduction to the three more common AI artificial intelligence security application technology content, we will further explore the deep technical development of AI artificial intelligence in security:
Multi-feature recognition technology
Generally, under a large amount of image data, it is necessary to screen criminal suspects from historical and real-time image data, such as a needle in a haystack, and multi-feature recognition technology allows the computer to automatically identify suspects from a large number of surveillance images through artificial intelligence. People, analyzing the personal characteristics of the data, and then automatically screening according to the characteristics of the suspects, not only greatly saves manpower and material resources, but also greatly shortens the time of the suspect's arrival. Now some manufacturers use advanced deep learning technology to develop various important features that can overcome the irresistible factors such as light and weather, and quickly and accurately identify individual characters, such as gender, age, hairstyle, clothing, body shape, whether to wear glasses, Whether to ride or carry anything. The individual character multi-feature recognition algorithm has a flexible layout method, which can customize the time axis and the recognition area range to achieve fast and accurate discrimination, and utilizes Intelligent Image Analysis (IVS) to assist the image server cluster in the monitoring system. Hundreds of video surveillance cameras perform 24-hour uninterrupted multi-feature analysis and retrieval to instantly find suspicious personnel and issue pre-alarm signals.
Gesture recognition technology
The gesture recognition technology is a walking posture for individual characters, and is a biological behavior feature technology that can be perceived at a long distance. Compared with other biometric technologies, gesture recognition has the advantages of non-contact, non-invasive, easy to perceive, difficult to hide and camouflage objects. Attitude analysis can also easily distinguish different behavior patterns of individual characters, such as walking, running, or carrying heavy loads. Based on these advantages, gesture recognition is especially suitable for access control systems, security monitoring, human-machine exchange, medical diagnosis, etc., especially in the security field has a wide range of applications and economic value.
The technical difficulty of attitude analysis lies in the stability of its characteristics, because a person's posture will change due to factors such as illness and injury, body fat and thinness, dressing and even wearing comfort. Some manufacturers have to overcome this problem. In particular, the machine depth learning method is added to the research and development, and the attitude vector diagram is used to describe the posture ordering, and the matching model is trained through the depth accumulation neural network. The trained cumulative neural network matching model can calculate the pose image to be recognized and the registered gesture images in order, compare the similarity of each pose vector graph, and then perform identity recognition according to the similarity degree. The gesture recognition application adopts the all-weather mode, which can accurately judge the identity of long-distance individual characters in a specific security situation. Therefore, researchers will inevitably need to build a large-scale attitude database in the future. Attitude recognition technology will help solve some of the problems of low image resolution and individual identity recognition, and provide users with important identification check clues.
3D camera technology
Height is one of the important data characteristics of the human body. In some specific places, such as the entrance to the scenic spot and the ticket gate of the station, there are clear requirements for height requirements. Although the traditional method of measuring the height using the scale tool is simple, it needs to be matched by the tested personnel, not only the speed is slow, but also the accuracy is poor. Ultrasonic, infrared, etc. can realize automatic measurement and high precision, but the environmental conditions are measured. There are more restrictions on the requirements, not suitable for use in public places, and 3D computer vision technology 3D camera can solve the above problems well, providing multi-scene, non-contact, automated measurement. The 3D camera uses the depth sensor to acquire the depth data and color information of the real scene, and establishes the correspondence between the depth data and the 3D coordinates through coordinate transformation, and then removes the algorithm by denoising, matching level and the like. Interference and reduce the error, and finally get the height and other data by 3D reconstruction.
The 3D camera does not need to be in contact with the object to be tested. When the object enters the measurement scene, it automatically collects and measures multiple target objects. After pairing the level, it has strong stability to the illumination, and can adapt to the illumination changes of the scene, thus also having high precision and Immediacy, the application in the field of security image monitoring will become more and more important. At present, advanced AI artificial intelligence analysis technology based on individual characters, gesture recognition and 3D camera, if combined to create a new generation of intelligent image analysis and monitoring software platform, will help the establishment of security monitoring system. At the same time, it plays a role as a model pioneer in data analysis.
Promote security future big data
Driven by the innovation of AI artificial intelligence analysis market, people explore the valuable data information in image monitoring, which is not only limited to the basic information of current people, things and things, but also depends on the strong research and development capabilities of manufacturers. Effectively supplementing the key information of security big data collection, not only brings more value-added data to the final big data platform, but also provides a continuous source of product development for deep AI artificial intelligence in the security industry data application. .
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