Deciphering the Superiority of VGG-16 in Transfer Learning for Breakthrough Precision in Human Pose

Abstract

In the present research, we investigate how well human position estimation can be accomplished by combining transfer learning (TL) with the VGG-16 deep convolutional neural network (DCNN). TL is a logical approach to take advantage of the streamlined training process and higher precision of cutting-edge models. We provide an experimental setup for comparing VGG-16's results with those of more conventional approaches for human posture assessment. We also detail an experiment conducted to assess TL's performance on VGG-16. We found that VGG-16 is capable of producing reliable estimates of human postures, and that the network's feature representation significantly enhanced the model's performance with TL. Our experimental results further show that VGG-16 outperformed conventional approaches, especially when dealing with complicated data. In addition, we discovered that TL with VGG-16 considerably improved the accuracy of posture estimation tasks, suggesting that the model may be used to speed up a variety of tasks related to stance estimation. Our findings suggest that transfer learning using VGG-16 might be a useful and time-saving method for human posture estimation.