First-Person Pose Recognition using Egocentric Workspaces
icon 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.

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Text Reference

Grégory Rogez, James S. Supan\vc i\vc  III, and Deva Ramanan. First-person pose recognition using egocentric workspaces. In CVPR. 2015.

BibTeX Reference

    AUTHOR = "Rogez, Gr{\'e}gory and Supan{\vc}i{\vc} III, James S. and Ramanan, Deva",
    TITLE = "First-Person Pose Recognition using Egocentric Workspaces",
    YEAR = "2015",
    tag = "people"