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Gamesourcing to Acquire Labeled Human Pose Estimation Data
| Content Provider | CiteSeerX |
|---|---|
| Author | Souvenir, Richard Hajja, Ayman Spurlock, Scott |
| Abstract | In this paper, we present a gamesourcing method for au-tomatically and rapidly acquiring labeled images of human poses to obtain ground truth data as input for human pose estimation from 2D images. Typically, these datasets are constructed manually through a tedious process of clicking on joint locations in images. By using a low-cost RGBD sensor, we capture synchronized, registered images, depth maps, and skeletons of users playing a movement-based game and automatically filter the data to keep a subset of unique poses. Using a recently-developed, learning-based human pose estimation method, we demonstrate how data collected in this manner is as suitable for use as training data as existing, manually-constructed data sets. 1. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
