2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), May 1, 2017
Estimating the 3D pose of a hand interacting with an object is a challenging task, harder than ha... more Estimating the 3D pose of a hand interacting with an object is a challenging task, harder than hand-only pose estimation as the object can cause heavy occlusion on the hand. We present a two stage discriminative approach using convolutional neural networks (CNN). The first stage classifies and segments the object pixels from a depth image containing the hand and object. This processed image is used to aid the second stage in estimating hand-object pose as it contains information regarding the object location and object occlusion. To the best of our knowledge, this is the first attempt at discriminative one shot hand-object pose estimation. We show that this approach outperforms the current state-of-theart and that the inclusion of a segmentation stage to learned discriminative single stage systems improves their performance.
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Papers by Duncan Goudie