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.

Human Pose Estimation, Transfer Learning, VGG-16, Deep Learning, Computer Vision, Image Recognition;