Recognition of Human Pose Utilizing General Adversarial Networks (GAN) Technologies - R. Regin; S. Suman Rajest; Shynu T; Steffi. R (European Journal of Life Safety and Stability (2660-9630), 2024)
In the subject of computer vision, one of the most significant problems to investigate is human pose estimation. In today’s world, there is a greater emphasis on automation, and we use surveillance and cameras to record everything that happens in our immediate environment or surroundings. The computer has a difficult time determining their stances for the purpose of the analytic process. Pose estimation is the process of anticipating the positions of the body parts or joints. Utilizations may include video monitoring, assisted living, advanced driver assistance systems, and sports analysis, among other potential applications. Because of their adaptability, humans are able to modify their stances regularly. An unsupervised machine learning approach known as a Generative Adversarial Network (GAN) is utilised by us in order to conduct an analysis of the postures assumed by human movement. With proper training, a GAN may be taught to produce images from random noises. The GAN is comprised of a generator and a discriminator. The generator is responsible for producing fake samples by utilising sounds, while the discriminator is responsible for attempting to differentiate between fake and real images. A basic input is used to generate a complex output, which is the goal of the GAN algorithm.