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• ALGORITHM FOR PRE-PROCESSING VIDEO IMAGES TO INCREASE THE ACCURACY OF SMALL OBJECT DETECTION

  V.V. Kovalev, N.E. Sergeev

  2021-12-24

  Abstract ▼

  Recognition of certain patterns in video images captured by a camera is carried out using
  training methods based on convolutional neural networks. The larger the number of images with
  multiple features and the more diverse the training sample of video images, the better the convolutional
  neural networks extract features from the sequence of video images that were not included in
  the training sample. This is a consequence of increasing the accuracy of detecting visual images on
  video images containing features of target images. However, there are limitations in improving the
  detection performance when the size of the image to be detected is much smaller than the background
  area, or when the image is described with little information. To solve problems of this kind, the authors
  of the article have developed an algorithm for the spatio-temporal integration of information
  about the movement of dynamic images. The algorithm processes a fixed number of video images at
  certain points in time and extracts new independent signs of motion of dynamic images based on
  space-time processing of video images. Further, it combines new local motion features with the original
  video image features. This allows you to add a motion feature of dynamic images while preserving
  the original image features that describe static images. Areas of the video image that characterize
  the motion feature are displayed in a «color» cluster. The use of pre-processing is aimed at improving the accuracy of pattern detection, provided there are dynamic visual images on a static background.
  If the camera is in scan mode, a static background can be provided with a video stabilizer.
  Experimentally, estimates of integral criteria for the accuracy of detection neural network algorithms
  have been obtained, showing an increase in the accuracy of detecting visual images using
  the algorithm for spatial-temporal integration of motion information.

• A SYSTEM FOR AUTOMATING DOCUMENT FLOW AND MONITORING ECONOMIC SECURITY INCIDENTS BASED ON ARTIFICIAL INTELLIGENCE TECHNOLOGIES

  А.Е. Anpilogova , V.А. Anpilogov

  31-41

  2025-07-24

  Abstract ▼

  Automation of document flow is a key element of process optimization and efficiency improvement. Automation of document flow based on artificial intelligence improves the management of economic security incidents by optimizing work processes and reducing costs. The transition to automated document flow in Russia is associated with a complex regulatory framework and large-scale implementation costs at  enterprises. Automation helps to comply with legal requirements and reduces the risks of legal and financial consequences. Integration of digital signatures increases the efficiency of document approval.
  The implementation of automation systems supports national digital transformation goals. Automation of document flow reduces dependence on paper processes and facilitates the creation of centralized digital repositories. The implementation of document automation systems requires a strategic approach and careful planning. Document automation provides time savings, reduced errors and increased compliance with regulatory standards. The article discusses the theoretical foundations of BPM, integration of digital technologies and regulatory aspects specific to Russia. The proposed system combines monitoring with AI and IoT, provides real-time data processing, automates the creation of legal documents and reports. The workflow automation system is based on data integration, artificial intelligence technologies and seamless solutions. The system combines monitoring technologies, facial recognition and behavior analysis algorithms, a centralized database and a communication module. The system generates reports and legal documents certified by QES and ensures interaction with law enforcement agencies and security services. Implementation results: a 30–40% reduction in operating costs and a 50% reduction in losses. The system complies with digital transformation standards and supports the modernization of the national economy.