Human body pose estimation and hand detection are two important tasks for
systems that perform computer vision-based sign language recognition(SLR). However, both tasks are challenging, especially when the input is color videos,
with no depth information...
Many algorithms have been proposed in the literature
for these tasks, and some of the most successful recent algorithms are based on
deep learning. In this paper, we introduce a dataset for human pose estimation
for SLR domain. We evaluate the performance of two deep learning based pose
estimation methods, by performing user-independent experiments on our dataset. We also perform transfer learning, and we obtain results that demonstrate that
transfer learning can improve pose estimation accuracy. The dataset and results
from these methods can create a useful baseline for future works.