  The availability of low-cost range sensors and the development of relatively
robust algorithms for the extraction of skeleton joint locations have inspired
many researchers to develop human activity recognition methods using the 3-D
data. In this paper, an effective method for the recognition of human
activities from the normalized joint trajectories is proposed. We represent the
actions as multidimensional signals and introduce a novel method for generating
action templates by averaging the samples in a "dynamic time" sense. Then in
order to deal with the variations in the speed and style of performing actions,
we warp the samples to the action templates by an efficient algorithm and
employ wavelet filters to extract meaningful spatiotemporal features. The
proposed method is also capable of modeling the human-object interactions, by
performing the template generation and temporal warping procedure via the joint
and object trajectories simultaneously. The experimental evaluation on several
challenging datasets demonstrates the effectiveness of our method compared to
the state-of-the-arts.
