  Pose detection is one of the fundamental steps for the recognition of human
actions. In this paper we propose a novel trainable detector for recognizing
human poses based on the analysis of the skeleton. The main idea is that a
skeleton pose can be described by the spatial arrangements of its joints.
Starting from this consideration, we propose a trainable pose detector, that
can be configured on a prototype skeleton in an automatic configuration
process. The result of the configuration is a model of the position of the
joints in the concerned skeleton. In the application phase, the joint positions
contained in the model are compared with the ones of their homologous joints in
the skeleton under test. The similarity of two skeletons is computed as a
combination of the position scores achieved by homologous joints. In this paper
we describe an action classification method based on the use of the proposed
trainable detectors to extract features from the skeletons. We performed
experiments on the publicly available MSDRA data set and the achieved results
confirm the effectiveness of the proposed approach.
