Domain Adaptation for Upper Body Pose Tracking in Signed TV Broadcasts
published: April 3, 2014, recorded: September 2013, views: 2098
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !