The objective of this work is to estimate upper body pose for signers in TV broadcasts. Given suitable training data, the pose is estimated using a random forest body joint detector. However, obtaining such training data can be costly.
The novelty of this paper is a method of transfer learning which is able to harness existing training data and use it for new domains. Our contributions are: (i) a method for adapting existing training data to generate new training data by synthesis for signers with different appearances, and (ii) a method for personalising training data. As a case study we show how the appearance of the arms for different clothing, specifically short and long sleeved clothes, can be modelled to obtain person-specific trackers.
We demonstrate that the transfer learning and person specific trackers significantly improve pose estimation performance.