  Successfully tracking the human body is an important perceptual challenge for
robots that must work around people. Existing methods fall into two broad
categories: geometric tracking and direct pose estimation using machine
learning. While recent work has shown direct estimation techniques can be quite
powerful, geometric tracking methods using point clouds can provide a very high
level of 3D accuracy which is necessary for many robotic applications. However
these approaches can have difficulty in clutter when large portions of the
subject are occluded. To overcome this limitation, we propose a solution based
on fully convolutional neural networks (FCN). We develop an optimized Fast-FCN
network architecture for our application which allows us to filter observed
point clouds and improve tracking accuracy while maintaining interactive frame
rates. We also show that this model can be trained with a limited number of
examples and almost no manual labelling by using an existing geometric tracker
and data augmentation to automatically generate segmentation maps. We
demonstrate the accuracy of our full system by comparing it against an existing
geometric tracker, and show significant improvement in these challenging
scenarios.
