With the development of Ping An City, video surveillance network sharing and more and more video materials, video structuring can make efficient use of these massive video information resources, solve some alarm monitoring problems in real time, quickly locate the human and vehicle information to be understood, and contribute to the risk assessment, event early warning and judgment of social security monitoring.

Video structuring refers to the establishment of a video big data structured platform according to the characteristics of people, vehicles, objects, colors, numbers and other attributes presented in the video picture. In particular, it provides a deeper structural analysis of the objectives of people, vehicles and non motor vehicles. After the video is structured, it is stored in the corresponding structured data warehouse.
In the application layer, the video structured analysis server can find the suspects and vehicle targets in a screenshot from the video database for the structured system in the video image, and can study and judge the behavior process of the target through the monitoring data collected from different locations.

Faceall-1000 face static/dynamic comparison server mainly provides face detection, face tracking, feature extraction and retrieval in photos and video streams. It supports the management of face images of millions and tens of millions of scales, high-speed feature extraction, and rapid large-scale concurrent search. The accuracy of comparison Results is high, and the number of returned Results is controllable. Besides, the product is flexible to deploy which can be used as a stand-alone machine or be gather together for forming a powerful and flexible cluster.

high cost performance and scalability
Face search speed in ten million level database is less than seconds
recognize faces accurately in complex and variable situations
Support more than 10 features recognition
Rich and friendly interfaces for business development

Face recognition technology can quickly and accurately identify the location of the face in the picture, which can be used for face beauty map, interesting face change, automatic special person search in news video, designated suspect in public security inspection, marriage faces, fate and other fields.

Face detection is to recognize the face in the image through the algorithm of deep learning and machine learning. The position information of face is given. Celebrity recognition can identify which celebrity the face in the figure is most like. The system will compare the selected face with the predefined 20000 celebrity faces and give the top 50 celebrities that are most similar to it. Compare the similarity of two faces to judge whether they are the same person, and the score is between 0-100%.

The service can automatically identify the target categories and scenes in the image (thousands of categories are customized, including clothing, home, animals and plants, violence, pornography, etc.) and can be used in product recommendation, automatic album analysis and sorting, Internet Security and other fields.

Using the deep learning method, quickly and accurately identify the category and scene of the target in the image, and the system will mark the 2000 categories (including pornographic image recognition and violent and terrorist image recognition) defined by us on the image. The probability output range is 0% ~ 100%. The higher the score, the closer the test picture is to the semantic concept. According to specific settings, up to 50 similar categories can be returned. Among them, pornographic identification is divided into four categories: pornographic, suspected pornographic, sexy and ordinary.