3D Hand Pose Detection in Egocentric {RGB-D} Images
icon We focus on the task of everyday hand pose estimation from egocentric viewpoints. For this task, we show that depth sensors are particularly informative for extracting near-field interactions of the camera wearer with his/her environment. Despite the recent advances in full-body pose estimation using Kinect-like sensors, reliable monocular hand pose estimation in RGB-D images is still an unsolved problem. The problem is considerably exacerbated when analyzing hands performing daily activities from a first-person viewpoint, due to severe occlusions arising from object manipulations and a limited field-of-view. Our system addresses these difficulties by exploiting strong priors over viewpoint and pose in a discriminative tracking-by-detection framework. Our priors are operationalized through a photorealistic synthetic model of egocentric scenes, which is used to generate training data for learning depth-based pose classifiers. We evaluate our approach on an annotated dataset of real egocentric object manipulation scenes and compare to both commercial and academic approaches. Our method provides state-of-the-art performance for both hand detection and pose estimation in egocentric RGB-D images.

Download: pdf

Text Reference

Grégory Rogez, James Steven Supan\vc i\vc , Maryam Khademi, J.M.M. Montiel, and Deva Ramanan. 3d hand pose detection in egocentric RGB-D images. CoRR, 2014. URL: http://arxiv.org/abs/1412.0065.

BibTeX Reference

    author = "Rogez, Gr{\'e}gory and Supan{\vc}i{\vc}, James Steven and Khademi, Maryam and Montiel, J.M.M. and Ramanan, Deva",
    title = "3D Hand Pose Detection in Egocentric {RGB-D} Images",
    journal = "CoRR",
    volume = "abs/1412.0065",
    year = "2014",
    url = "http://arxiv.org/abs/1412.0065",
    tag = "people"