  Scene flow describes the motion of 3D objects in real world and potentially
could be the basis of a good feature for 3D action recognition. However, its
use for action recognition, especially in the context of convolutional neural
networks (ConvNets), has not been previously studied. In this paper, we propose
the extraction and use of scene flow for action recognition from RGB-D data.
Previous works have considered the depth and RGB modalities as separate
channels and extract features for later fusion. We take a different approach
and consider the modalities as one entity, thus allowing feature extraction for
action recognition at the beginning. Two key questions about the use of scene
flow for action recognition are addressed: how to organize the scene flow
vectors and how to represent the long term dynamics of videos based on scene
flow. In order to calculate the scene flow correctly on the available datasets,
we propose an effective self-calibration method to align the RGB and depth data
spatially without knowledge of the camera parameters. Based on the scene flow
vectors, we propose a new representation, namely, Scene Flow to Action Map
(SFAM), that describes several long term spatio-temporal dynamics for action
recognition. We adopt a channel transform kernel to transform the scene flow
vectors to an optimal color space analogous to RGB. This transformation takes
better advantage of the trained ConvNets models over ImageNet. Experimental
results indicate that this new representation can surpass the performance of
state-of-the-art methods on two large public datasets.
