  At present, most high-accuracy single-person pose estimation methods have
high computational complexity and insufficient real-time performance due to the
complex structure of the network model. However, a single-person pose
estimation method with high real-time performance also needs to improve its
accuracy due to the simple structure of the network model. It is currently
difficult to achieve both high accuracy and real-time performance in
single-person pose estimation. For use in human-machine cooperative operations,
this paper proposes a single-person upper limb pose estimation method based on
an end-to-end approach for accurate and real-time limb pose estimation. Using
the stacked hourglass network model, a single-person upper limb skeleton key
point detection model was designed.Deconvolution was employed to replace the
up-sampling operation of the hourglass module in the original model, solving
the problem of rough feature maps. Integral regression was used to calculate
the position coordinates of key points of the skeleton, reducing quantization
errors and calculations. Experiments showed that the developed single-person
upper limb skeleton key point detection model achieves high accuracy and that
the pose estimation method based on the end-to-end approach provides high
accuracy and real-time performance.
