Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition
This work addresses sign language recognition for accessibility applications, but it is incremental as it builds on existing methods with a new dataset.
The paper tackles the challenge of pose estimation for sign language recognition by introducing a new dataset and evaluating two deep learning methods, showing that transfer learning improves accuracy with concrete results.
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.