First-Person Pose Recognition Using Egocentric Workspaces

Gregory Rogez, James S. Supancic III, Deva Ramanan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4325-4333

Abstract


We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider pose estimation during everyday interactions with objects. Past work shows that strong pose+viewpoint priors and depth-based features are crucial for robust performance. In egocentric views, hands and arms are observable within a well defined volume in front of the camera. We call this volume an egocentric workspace. A notable property is that hand appearance correlates with workspace location. To exploit this correlation, we classify arm+hand configurations in a global egocentric coordinate frame, rather than a local scanning window. This greatly simplify the architecture and improves performance. We propose an efficient pipeline which 1) generates synthetic workspace exemplars for training using a virtual chest-mounted camera whose intrinsic parameters match our physical camera, 2) computes perspective-aware depth features on this entire volume and 3) recognizes discrete arm+hand pose classes through a sparse multi-class SVM. We achieve state-of-the-art hand pose recognition performance from egocentric RGB-D images in real-time.

Related Material


[pdf]
[bibtex]
@InProceedings{Rogez_2015_CVPR,
author = {Rogez, Gregory and Supancic, III, James S. and Ramanan, Deva},
title = {First-Person Pose Recognition Using Egocentric Workspaces},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}