With the development of China's industry strategy, the market share of the machine vision industry will gradually increase, and its market competitiveness will also gradually strengthen. According to data from the "China Machine Vision Industry Development Prospect and Investment Analysis Report" by Qianzhan Industry Research Institute, the global market size of machine vision systems and components reached US$4.2 billion in 2017, and is expected to exceed US$5 billion in 2018, indicating rapid development in the global machine vision industry.
With the development of AI machine vision technology, the security industry has ushered in a new era of intelligence and informatization. As one of the main battlegrounds for machine vision applications, the application of core machine vision technologies such as target recognition, target tracking, video summarization, and video retrieval has brought tremendous changes to the security industry. In addition, the implementation speed of technologies such as biometrics and vehicle recognition is also accelerating, and the market mechanism is becoming increasingly mature.
Target recognition
Target recognition technology and stable tracking methods are among the key factors in the development of AI machine vision. It has been widely applied in many fields (such as fingerprint recognition, facial recognition, and iris recognition for identity verification, as well as vehicle detection and license plate recognition in intelligent traffic management).
Target tracking
Moving target tracking is the process of determining the position of the same object in different frames of an image sequence. Its main working principle involves selecting appropriate target features and employing suitable search methods. Based on matching principles, existing tracking methods can be categorized into model-based, region-based, feature-based, and contour-based tracking.
Visual analysis
Visual analytics technology involves identifying and tracking targets, then acquiring information such as their appearance time, movement trajectory, and color. By analyzing this information, it identifies dangerous, illegal, or suspicious targets in videos and provides real-time alerts, early warnings, storage, and post-event retrieval for these behaviors and targets.
In the application of visual analytics, the most important technologies are intelligent video surveillance and intelligent video retrieval.
The two technologies are similar, the main difference being:
1. Intelligent video surveillance processes the video footage collected in real time and issues an alarm when a dangerous event or suspicious person is detected.
2. Intelligent video retrieval technology processes stored videos of past events. It quickly analyzes the videos to identify dangerous events, suspicious individuals, and information on each target of interest. Users can then select events of interest or define the attributes of targets of interest, and the system can quickly find the events or targets of interest to the user.
Intelligent video retrieval first requires utilizing the detection technology of intelligent video surveillance to detect abnormal events. Furthermore, based on moving target detection and tracking, intelligent video retrieval also needs to obtain various information about targets such as people and vehicles, including facial features, color, speed, and quantity. In this way, intelligent video retrieval can not only search for abnormal events but also perform searches based on the target's appearance and end time, color, speed, quantity, and facial information.
In addition, the system can provide a spatiotemporal distribution map of events and targets, making it easier for users to find time periods and events of interest. With tens of thousands of monitoring terminals nowadays, finding the events and targets of interest from such massive amounts of data requires the assistance of intelligent video retrieval technology.
Video surveillance technology is an emerging application area in the field of machine vision, a cutting-edge topic that has attracted much attention, and a culmination of multiple disciplines such as computer science, machine vision, image engineering, pattern recognition, and artificial intelligence.
It's conceivable that incorporating machine vision and image processing technologies could break through existing limitations and allow for the design of a real-time video surveillance system. This system, while providing video monitoring, utilizes machine vision technology to add video change detection and automatic recording functions. The system can automatically identify scene changes, detect and lock onto moving targets, and simultaneously issue warnings and activate the storage device. This not only saves significant storage space and improves monitoring storage efficiency, reducing unnecessary playback, but also enhances the relevance of the data.
In July 2017, the State Council issued the "New Generation Artificial Intelligence Development Plan," officially elevating artificial intelligence to a national strategy. With favorable national policies, AI+security continues to lead new trends in the industry market. As the latest sector to implement artificial intelligence technology, it will undoubtedly be driven and disrupted by AI in the future. This is prompting traditional security companies to rapidly achieve intelligent transformation, while AI startups and chip manufacturers, leveraging their algorithmic advantages, are also accelerating their expansion into the security sector.
The development of AI will lead to a massive number of AI machines in the security field in the future. The evolution of these machines will require each AI machine to have eyes, and the most crucial aspect of this evolution from ordinary cameras to eyes is environmental adaptability. If a camera can adapt to various environments like our eyes and can collect and recognize images, then the evolution of the camera will be complete.
