An efficient approach for human pose detection using blaze pose algorithm to improve accuracy in comparison with convolutional neural networks.

The primary objective of this study is to evaluate the efficacy of the Blaze Pose Algorithm, a new method for human pose detection, in comparison to that of Convolutional Neural Networks. Unlike the current method, which relies on static neural networks, Blazepose is a novel deep learning model for estimating human poses. Pretest power is 0.8, and there are 163 participants in each group. Data was gathered from the MPII Human Pose Dataset, which contains over 25,000 photos taken from various web videos. Researchers found that Blazepose's accuracy was slightly higher than that of a traditional neural network (90.3% vs. 40%). Independent sample T-test results show a 95% level of confidence that there is a statistically significant difference between the proposed and existing groups, with a significance value of P = 0.001 (P 0.05) 2-tailed. The Blazepose outperforms the traditional neural network in terms of improved accuracy, as seen by the comparison findings. [ABSTRACT FROM AUTHOR]