Human body part estimation from depth images via spatially-constrained deep learning

We integrate spatial part layout into supervised learning of the deep ConvNets.Spatial learning for unsupervised pre-training of the deep ConvNets is proposed.We are the first to apply deep learning to the body segmentation from depth images. Object recognition, human pose estimation and scene recognition are applications which are frequently solved through a decomposition into a collection of parts. The resulting local representation has significant advantages, especially in the case of occlusions and when the subject is non-rigid. Detection and recognition require modelling the appearance of the different object parts as well as their spatial layout. This representation has been particularly successful in body part estimation from depth images.Integrating the spatial layout of parts may require the minimization of complex energy functions. This is prohibitive in most real world applications and therefore often omitted. However, ignoring the spatial layout puts all the burden on the classifier, whose only available information is local appearance. We propose a new method to integrate spatial layout into parts classification without costly pairwise terms during testing. Spatial relationships are exploited in the training algorithm, but not during testing. As with competing methods, the proposed method classifies pixels independently, which makes real-time processing possible. We show that training a classifier with spatial relationships increases generalization performance when compared to classical training minimizing classification error on the training set. We present an application to human body part estimation from depth images.