A NEURAL NETWORK MODEL FOR RECOGNIZING HUMAN POSES BASED ON INFORMATION OBTAINED FROM VIDEO IMAGES

Abstract

Nowadays, there are several ways of recognizing and identifying a person, and these keep enhancing day by day. However, cases of falsifi cation of these methods can also be observed.
Modern video surveillance systems keep on developing, and satellite systems enable us to photograph everything that happens in a certain area and analyze eff ectively the data obtained using neural networks. Video images help to monitor motions of people, detect illegal entries into prohibited areas, as well as to identify remaining criminals by means of photos taken from cameras and control exit and/ or entry of wanted criminals to foreign countries by acquiring other biometric data. This system serves to catch criminals at airports, railway stations, seaports; it automatically counts numbers of people in a line or in a crowd and analyzes the nature of their movements, which lessens the amount of subjective human intervention and reduces time required for data processing.
Moreover, neural network-based pose estimation system is now being used widely in sports, and this can distinguish between athletes and change their behavior based on their personal characteristics