  A challenge of skeleton-based action recognition is the difficulty to
classify actions with similar motions and object-related actions. Visual clues
from other streams help in that regard. RGB data are sensible to illumination
conditions, thus unusable in the dark. To alleviate this issue and still
benefit from a visual stream, we propose a modular network (FUSION) combining
skeleton and infrared data. A 2D convolutional neural network (CNN) is used as
a pose module to extract features from skeleton data. A 3D CNN is used as an
infrared module to extract visual cues from videos. Both feature vectors are
then concatenated and exploited conjointly using a multilayer perceptron (MLP).
Skeleton data also condition the infrared videos, providing a crop around the
performing subjects and thus virtually focusing the attention of the infrared
module. Ablation studies show that using pre-trained networks on other large
scale datasets as our modules and data augmentation yield considerable
improvements on the action classification accuracy. The strong contribution of
our cropping strategy is also demonstrated. We evaluate our method on the NTU
RGB+D dataset, the largest dataset for human action recognition from depth
cameras, and report state-of-the-art performances.
