Surveillance video image technology introduction
With the improvement of people's living standards and the development of science and technology, video surveillance technology is increasingly being applied to people's lives. Surveillance networks are gradually covering people's living areas, so to what extent has the current video surveillance processing technology developed?
Digital video and digital images have a higher resolution than traditional images and videos, are easy to handle, and easy to operate and organize. However, due to factors such as insufficient performance of some devices and limitations of objective conditions, in actual video surveillance applications, problems such as blurred video images and unavailability of key information may still occur. In the process of video image processing, due to operational technical problems or objective factors, etc., it brings some negative effects to the application of video image processing technology, reducing the level and quality of processing technology.
The process of video image processing will involve the collection, transmission, processing, display and playback of video image data. These processes together form a system's overall cycle, which can be operated continuously. Within the scope of video image processing technology, the most important ones include image compression technology and video image processing technology. Currently, mainstream video image processing technologies on the market include: intelligent analysis processing, video fog enhancement technology, wide dynamic processing, and super-resolution processing. The following four processing technologies are introduced below.
Intelligent analysis and processing technologyIntelligent video analysis technology is an important means to solve the problems of big data screening and retrieval technology in the field of video surveillance. At present, domestic intelligent analysis technology can be divided into two categories: one is to detect the movement of objects in the picture through methods such as foreground extraction, and set rules to distinguish different behaviors, such as mixing lines, leftover items, perimeters, etc. The other is the use of pattern recognition technology to target the objects that need to be monitored in the picture, so as to achieve the detection of specific objects in the video and related applications, such as vehicle detection, flow statistics, face detection and other applications .
Video fog enhancement technologyVideo fog enhancement technology generally refers to making the hazy image caused by fog, moisture and dust become clear, emphasizing some interesting features in the image, suppressing the uninteresting features, and improving the quality of the image. Information The amount is more abundant. Due to bad conditions such as haze weather and rain, snow, strong light, dark light, etc., the image contrast of the video surveillance image is low, the resolution is low, the image is blurred, and the features cannot be identified. Application provides good conditions.
Digital image width dynamic algorithmThe wide dynamic range is a basic feature in digital image processing, and occupies an important position in image and vision restoration, which is related to the imaging quality of the final image. Its dynamic range is mainly determined by the amount of protection signal and the average noise ratio, where the dynamic range can be defined from the perspective of light energy.
Digital signal processing will be affected and affected by the exposure effect, light intensity and intensity in the exposure. The dynamic range is closely related to the depth of the pattern. If the dynamic range of the image is wide, the brightness change is more obvious when the image is processed, but if the dynamic range is narrow, the change in brightness and darkness is not obvious when the brightness is converted. At present, the wide dynamic range of images is widely used in video surveillance, medical imaging and other fields.
Super-resolution reconstruction technologyThe most direct way to increase the image resolution is to increase the sensor density of the acquisition device. However, high-density image sensors are relatively expensive and difficult to withstand in general applications; on the other hand, because imaging systems are limited by the density of their sensor arrays, they are now close to the limit.
The effective way to solve this problem is to use the software method based on signal processing to improve the spatial resolution of the image, that is, super-resolution (SR: Super-ResoluTIon) image reconstruction, the core idea is to use time bandwidth (to obtain the same scene Multi-frame image sequence) in exchange for spatial resolution to achieve the conversion of temporal resolution to spatial resolution, so that the visual effect of the reconstructed image exceeds that of any low-resolution image.
With the increasing demand for the quality of surveillance images, improving the practical value of surveillance images has become a new requirement put forward by the society to the entire security industry. In this form, the current mainstream video image processing technology must keep pace with the times to meet the changing needs of users.
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