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Security AI big data full process analysis
For security AI, it seems to be AI, in fact, the last is big data, big data is the basis of intelligence. Artificial intelligence, deep learning, machine learning, and big data applications in security AI are all about the collection, modeling, and application of big data. This article roughly talks about security AI, the process and links of big data, so that everyone has a general impression.
Security AI big data process three links
1, data collection
Data collection, there is data acquisition, this is the source of the data, this data in the security AI is derived from the video stream in the video surveillance system, of course, to the big say security, but also a lot of content, but basically by video surveillance Core, here mainly refers to the video surveillance system.
2, data preprocessing
For the collected real-time or historical video, it can only be seen and can not be applied. To be called, it must be structured. First, the video stream is decoded, the video stream is restored into a picture, and the picture is pre-processed. It may be that different companies do not agree on the content of the steps involved in the pre-processing. I will refer to the technical person in charge of Ansoft. The target is first cleaned of the garbage, cleaned out fuzzy, out of size, unrecognized objects, no target objects, and so on.
Of course, some scenes may have only such images, which requires additional image processing methods, which are related to our theme but not the same thing. This way we can get an image that basically meets the requirements. Then the target object in these images is detected and segmented, and the size of the target is changed to be the same as the standard picture size. The target object includes the human form, the face, the car shape, etc., so that the training model can be taken.
3. Model training
Identifying and extracting the target objects in the image. In the security AI, the required structured description is more specific. For example, the description of the person includes gender, age, hair style, hair accessories, jacket style characteristics, A series of descriptions of the style characteristics of the blouse, the style characteristics of the shoes and hats, the characteristics of the vehicle, the characteristics of the personal belongings, and the characteristics of the peers. The description of the car includes the license plate number, the brand, the body color, the vehicle brand, the vehicle type, and the vehicle features (such as: annual inspection mark, hanging ornament, tissue box, sun visor).
With these recognition models, video data can be classified and stored by techniques such as semantic analysis, and the business processing can be performed through the intelligent analysis function of the back-end server, and the information of people, vehicles and objects can be separated from the data. In this way, you can perform quick search, conditional search (person), map search, and with the location, time and other data of the picture, you can query the track, and then match the accommodation, shou machine number in the big security system. Big data such as tickets and tickets, basically suspects are unable to escape from the wings, this will be a hundred thousand times improvement in the efficiency of the crime. This is the real value of security AI. This was discussed in the previous article "Security AI Massive Landing, Analysis of First-Line Scenes and Key Points of Innovation".
Security AI data preprocessing technology and method
Current common data preprocessing techniques
1) Data cleaning
Data cleanup routines "clean up data" by filling in missing values, smoothing noise data, identifying or deleting outliers, and resolving inconsistencies.
2) Data integration
The data integration process integrates data from multiple data sources.
3) Data protocol
The data specification is to get a simplified representation of the data set. Data protocols include statutes and numerical conventions.
4) Data transformation
By transforming methods such as normalization, data discretization, and concept stratification, data mining can be performed at multiple levels of abstraction. The data transformation operation is an additional preprocessing process that improves the data mining effect.
Data cleaning method
1) Missing value
For the processing of missing values, it is generally possible to make up for it. If you can't make up, you will discard it. The usual treatments are: ignoring tuples, manually filling in missing values, filling in missing values ​​with a global variable, filling missing values ​​with the center measure of the attribute, and using the attribute mean or median of all samples of the same class as the given tuple. Count, fill in missing values ​​with the most likely values.
2) Noise data
Noise is the random error or variance of the measured variable. Techniques for removing noise and making the data "smooth" include binning, regression, and outlier analysis.
3) Data cleaning process
This part mainly includes data preprocessing, cleaning methods, verification cleaning methods, execution cleaning tools and data archiving. The principle of data cleansing is to analyze the causes and existing forms of "invalid data", use existing technical means and methods to clean up, and convert "invalid data" into data that meets data quality or application requirements, thereby improving the data set. Data quality. Commonly used tools are Excel, Access, SPSS Modeler, SAS, SPSS Statistics, and so on.
4) Statistical analysis of model construction data
Data statistics provide the basis for model construction. Only through the statistical analysis of data can the hidden rules of data be explored. Deep learning is meaningful, and artificial intelligence is possible. Data statistics include data analysis and results analysis. The basic analysis methods are: comparative analysis, group analysis, cross analysis, factor analysis, structural analysis, funnel analysis, matrix correlation analysis, comprehensive evaluation analysis. Wait.
Advanced analysis methods include: principal component analysis, factor analysis, correspondence analysis, correlation analysis, regression analysis, cluster analysis, discriminant analysis, time series, etc. These categories are not used exclusively, they are often mixed, and then some combination models are selected from further analysis and comparison.
5) Data visualization
Data visualization is demonstrated by some visual graphics or report forms to enhance the understanding of the analysis results. Further data reanalysis is performed on the results, so that the entire business process forms a closed loop. Only closed-loop data can really play the role of deep learning.
Security AI big data application
The application of security big data is centered around improving the crime detection rate and improving the efficiency of police work. To develop excellent applications based on security data, it is necessary to have an in-depth understanding of the police work flow, from picking up the police and on-site investigation. , intelligence research and judgment, emergency command, associated collision, synthetic zuo battle, and then to the interconnection of various types of jing equipment, and then to the cooperation between the police, and then to the integration of the police database. These must be understood in detail in order to find differences in the application of criminal investigation, prevention and control, and traffic management.
For example, for the tracking of suspects, it may be the face, but most of the time the video is not recognizable. This is the fact that face recognition is only useful in card point scenes such as stations, airports, banks, etc. However, most fugitives are somewhat anti-reconnaissance. They don’t just walk around when they see the camera, they just deliberately block it, or simply wait until dark.
At this time, the main function that can be used is to track the shape of the suspect, and to map and map with the selection of the range, the selection of the time period, and then the characteristics of the accompanying person, the characteristics of the accompanying items, and the characteristics of the tools, can effectively The suspects conduct high-efficiency screening and conduct trajectory research and judgment through comprehensive intelligence. In this case, the suspect is almost the top of the shackles, and there is no way to go to heaven. If the children’s elderly people are lost again, and they want to find out what is happening in minutes, there will be no more cases in which a large number of police forces will be launched along the street, and it will take dozens of hours. In this way, will the relevant departments dislike it?
After all, big data is ultimately serving applications. Only by finally improving work efficiency and crime detection rate can we prove the value and significance of security AI. This requires us to understand both the AI ​​business and the business. Leaving the two, I want to let the security AI get a large-scale promotion.