Algorithmated CCTV system for hazards recognize
In the article, the preliminary results of creating an automated video surveillance system for objects of increased danger recognizing, for example, when organizing operational video surveillance of security measures at railway infrastructure facilitiesin, sensitive organizations (military plants and institutions, research institutes,etc.) are proposed. The process of operational video surveillance based on a neural network is proposed as an improvement of existing systems. To date, there are very few such developments. An algorithm for detecting objects and the architecture of a neural network is proposed. A computer application was implemented in the Python 3.6 programming language, using the TensorFlow library, and the basic YOLOv3 architecture, and training was conducted on images of dangerous objects. The application was tested on video, with the image of real knives, grenades, pistols, machine guns and mock-ups of dangerous objects, the possibilities of improving the characteristics of the system are considered.