3D Hand Pose Estimation Using Randomized Decision Forest With Segmentation Index Points

Peiyi Li, Haibin Ling, Xi Li, Chunyuan Liao; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 819-827

Abstract


In this paper, we propose a real-time 3D hand pose estimation algorithm using the randomized decision forest framework. Our algorithm takes a depth image as input and generates a set of skeletal joints as output. Previous decision forest-based methods often give labels to all points in a point cloud at a very early stage and vote for the joint locations. By contrast, our algorithm only tracks a set of more flexible virtual landmark points, named segmentation index points (SIPs), before reaching the final decision at a leaf node. Roughly speaking, a SIP represents the centroid of a subset of skeletal joints, which are to be located at the leaves of the branch expanded from the SIP. Inspired by recent latent regression forest-based hand pose estimation framework (Tang et al. 2014), we integrate SIP into the framework with several important improvements: First, we devise a new forest growing strategy, whose decision is made using a randomized feature guided by SIPs. Second, we speed-up the training procedure since only SIPs, not the skeletal joints, are estimated at non-leaf nodes. Third, the experimental results on public benchmark datasets show clearly the advantage of the proposed algorithm over previous state-of-the-art methods, and our algorithm runs at 55.5 fps on a normal CPU without parallelism.

Related Material


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[bibtex]
@InProceedings{Li_2015_ICCV,
author = {Li, Peiyi and Ling, Haibin and Li, Xi and Liao, Chunyuan},
title = {3D Hand Pose Estimation Using Randomized Decision Forest With Segmentation Index Points},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}