Nov 18, 2021 View: 947
With the penetration and in-depth application of artificial intelligence in the security industry, the current security industry has shown a new trend of "no AI, no security". The AI-based products of various security manufacturers have become an indisputable fact at present, and they have also become a new strategic high ground for various manufacturers.
Since artificial intelligence has gradually deepened its landing in the security industry, the product form and application mode of AI in the security field, especially the video surveillance field, have also begun to stabilize. AI technology in the security industry is mainly applied in face recognition, pedestrian recognition, behavior recognition, vehicle recognition, structured analysis, large-scale structured and semi-structured video information retrieval.
According to the application scenarios, the application of artificial intelligence in the security industry is mainly divided into bayonet scenarios and non-bayonet scenarios.
Bayonet scenes are mainly face/person bayonet and vehicle bayonet. In such scenes, the camera's acquisition position, light, angle and other conditions are controllable.
Non-bayonet scenes refer to common security surveillance scenes, and there are no strict requirements on the installation position and angle of the camera and the collected light.
Bayonet scenes account for about 1%-3% of the total number of video surveillance cameras, and the rest are non-bayonet scene surveillance videos.
Strictly speaking, artificial intelligence is not a strict academic term. In the security industry, AI mostly refers to machine learning based on video images, which is a pattern recognition AI. Michael Jordan, a leading scholar in the field of machine learning, said that currently commonly used AI has the following three categories:
First, human-imitative AI is a series of recent successful cases of deep learning, including computer vision, speech recognition, and game artificial intelligence.
The second is Intelligence Augmentation (IA) search engines that can enhance human memory and machine translation that enhances human communication capabilities.
The third is the field of Intelligent Infrastructure (II), which aims to use computer technology to make the environment of human life safer and more interesting, and to provide support for human life from the perspective of infrastructure. The Internet of Things is an early version of this concept. The current smart city is a typical application of II.
The facial identity confirmation application belongs to the human-like AI application in the bayonet scene, represented by the personnel of the public security industry. Face capture cameras are installed at key monitoring points, and the captured faces are compared and recognized through the back-end face recognition server. To determine the identity of the person.
At present, there are two types of face identity verification: comparison and recognition with the face blacklist database and comparison and recognition with the static face database.
Face blacklist dynamic deployment control applications mainly use face capture cameras to detect and capture face photos using deep learning models from high-definition/ultra-high-definition video images, and then extract the deep learning feature vectors of the faces to compare and compare the faces of the blacklist library. Realize alarm prompt; static face comparison refers to the use of deep learning models to detect and capture face pictures, and then compare and recognize the extracted face deep learning feature vector with the face in the static library to confirm the identity of the face.
Face authentication is an application in a bayonet scenario, and a whitelist application is a facial authentication application. Face whitelist applications have been implemented in many industries, such as face access control, face speed access door, face attendance, and identification verification, etc., and are widely used in scenarios such as enterprises, institutions, and various parks.
In addition to realizing basic facial recognition applications, face authentication also uses live detection and other means to prevent fake behaviors such as photos, videos, masks, etc., and effectively ensure the safety control of entrance and exit personnel and daily personnel management.
Vehicle recognition application belongs to bayonet scene application. Vehicle recognition technology is one of the most mature and effective technologies used in public security actual combat.
With the help of vehicle bayonet points on major traffic roads all over the country, license plate recognition has made "finding people by car" a reality, and successfully assisted the police in cracking various cases. Vehicle recognition technology has developed from the primary application stage of vehicle recognition based on license plate to the precise application stage of vehicle recognition such as model recognition and license plate recognition.
Video structured applications are generally non-bayonet applications. In the application of video structured analysis and fast retrieval, the function of video structured service is to classify and detect motor vehicles, non-motor vehicles, pedestrians and other moving targets in the video, and to identify their characteristic attributes. Extract the small target image and the large image of the scene into the storage device to facilitate subsequent quick query and intelligent retrieval. By quickly analyzing and extracting the characteristic attribute information of the target of interest in the video through the video structured business, users can efficiently obtain clues related to the case, and promote the transition of video data from "clear" to "understandable" in the era of security protection. stage.
Behavior analysis can assist security applications. Analyze and process the abnormal behavior of personnel through the behavior analysis system, which can be applied to machine identification of behaviors such as key area prevention, important item monitoring, suspicious leftover of dangerous goods, etc., and it can also alarm abnormal behavior of personnel, which greatly improves video surveillance Application efficiency.
It can also realize group situation analysis, such as crowd density analysis, crowd gathering analysis, etc., analyze the situation of key areas or places where people gather to prevent crowd incidents, and achieve early warning and timely disposal.
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