Conceptual structure of a decision support system for situational video analytics for multi-object recognition

Information is provided on the growing interest in situational video analytics (SVA) technologies in various fields of activity. The paper describes a trend towards the implementation of SVA systems based on cognitive artificial intelligence technologies, which make it possible to detect an object and its location in a video stream in real time with a high degree of accuracy. The architecture of a decision support system (DSS) with the possibility of multi-object recognition is proposed. The key logical components of the DSS in the field of SVA are highlighted, as well as the functions and purpose of each component are described. The role of the decision maker for implementing multi-object recognition when searching for an object in a video stream is particularly emphasized. The classification of neural networks by types and fields of application is given, and it is revealed that convolutional neural networks are used to solve video analytics problems. Examples of using the YOLOv5 convolutional model in the DSS model database management system in IAS tasks for detecting the presence of objects on video data are considered. A block diagram of the SVA object recognition algorithm has been developed. A series of experiments has been conducted to train a convolutional neural network model on a unique dataset, on an expanded dataset, and with a new additional object. This organization of experiments is aimed at improving the quality and accuracy of object recognition and exploring the possibility of multi-object recognition. As a result of the experiment, the final trained neural network model was tested and its potential capabilities were analyzed for use in the SVA DSS, taking into account the accuracy of the model. The accuracy was 91.6% for the validation set containing 2 objects. The results obtained using a trained neural network of the YOLOv5 architecture confirms the importance of convolutional neural networks as a key component of the database of SVA DSS models.