  Different from RGB videos, depth data in RGB-D videos provide key
complementary information for tristimulus visual data which potentially could
achieve accuracy improvement for action recognition. However, most of the
existing action recognition models solely using RGB videos limit the
performance capacity. Additionally, the state-of-the-art action recognition
models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous
parameters suffering from computational inefficiency. In this paper, we propose
a series of 3D light-weight architectures for action recognition based on RGB-D
data. Compared with conventional 3D-CNN models, the proposed light-weight
3D-CNNs have considerably less parameters involving lower computation cost,
while it results in favorable recognition performance. Experimental results on
two public benchmark datasets show that our models can approximate or
outperform the state-of-the-art approaches. Specifically, on the RGB+D-NTU
(NTU) dataset, we achieve 93.2% and 97.6% for cross-subject and cross-view
measurement, and on the Northwestern-UCLA Multiview Action 3D (N-UCLA) dataset,
we achieve 95.5% accuracy of cross-view.
