  Dynamic imaging is a recently proposed action description paradigm for
simultaneously capturing motion and temporal evolution information,
particularly in the context of deep convolutional neural networks (CNNs).
Compared with optical flow for motion characterization, dynamic imaging
exhibits superior efficiency and compactness. Inspired by the success of
dynamic imaging in RGB video, this study extends it to the depth domain. To
better exploit three-dimensional (3D) characteristics, multi-view dynamic
images are proposed. In particular, the raw depth video is densely projected
with respect to different virtual imaging viewpoints by rotating the virtual
camera within the 3D space. Subsequently, dynamic images are extracted from the
obtained multi-view depth videos and multi-view dynamic images are thus
constructed from these images. Accordingly, more view-tolerant visual cues can
be involved. A novel CNN model is then proposed to perform feature learning on
multi-view dynamic images. Particularly, the dynamic images from different
views share the same convolutional layers but correspond to different fully
connected layers. This is aimed at enhancing the tuning effectiveness on
shallow convolutional layers by alleviating the gradient vanishing problem.
Moreover, as the spatial occurrence variation of the actions may impair the
CNN, an action proposal approach is also put forth. In experiments, the
proposed approach can achieve state-of-the-art performance on three challenging
datasets.
