  Skeleton-based action recognition receives the attention of many researchers
as it is robust to viewpoint and illumination changes, and its processing is
much more efficient than the processing of video frames. With the emergence of
deep learning models, it has become very popular to represent the skeleton data
in pseudo-image form and apply CNN for action recognition. Thereafter, studies
concentrated on finding effective methods for forming pseudo-images. Recently,
attention networks, more specifically transformers have provided promising
results in various vision problems. In this study, the effectiveness of VIT for
skeleton-based action recognition is examined and its robustness on the
pseudo-image representation scheme is investigated. To this end, a three-level
architecture, SkelVit is proposed, which forms a set of pseudo images, applies
a classifier on each of the representations, and combines their results to find
the final action class. The performance of SkelVit is examined thoroughly via a
set of experiments. First, the sensitivity of the system to representation is
investigated by comparing it with two of the state-of-the-art pseudo-image
representation methods. Then, the classifiers of SkelVit are realized in two
experimental setups by CNNs and VITs, and their performances are compared. In
the final experimental setup, the contribution of combining classifiers is
examined by applying the model with a different number of classifiers.
Experimental studies reveal that the proposed system with its lightweight
representation scheme achieves better results than the state-of-the-art
methods. It is also observed that the vision transformer is less sensitive to
the initial pseudo-image representation compared to CNN. Nevertheless, even
with the vision transformer, the recognition performance can be further
improved by the consensus of classifiers.
