Abstract
In action prediction (early action recognition), the goal
is to predict the class label of an ongoing action using its
observed part so far. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion
dynamics in temporal dimension via a sliding window over
the time axis. As there are significant temporal scale variations of the observed part of the ongoing action at different
progress levels, we propose a novel window scale selection
scheme to make our network focus on the performed part of
the ongoing action and try to suppress the noise from the
previous actions at each time step. Furthermore, an activation sharing scheme is proposed to deal with the overlapping computations among the adjacent steps, which allows
our model to run more efficiently. The extensive experiments
on two challenging datasets show the effectiveness of the
proposed action prediction framework