An Evaluation of Gamesourced Data for Human Pose Estimation

Gamesourcing has emerged as an approach for rapidly acquiring labeled data for learning-based, computer vision recognition algorithms. In this article, we present an approach for using RGB-D sensors to acquire annotated training data for human pose estimation from 2D images. Unlike other gamesourcing approaches, our method does not require a specific game, but runs alongside any gesture-based game using RGB-D sensors. The automatically generated datasets resulting from this approach contain joint estimates within a few pixel units of manually labeled data, and a gamesourced dataset created using a relatively small number of players, games, and locations performs as well as large-scale, manually annotated datasets when used as training data with recent learning-based human pose estimation methods for 2D images.