  Human activity recognition is one of the important research topics in
computer vision and video understanding. It is often assumed that high quality
video sequences are available for recognition. However, relaxing such a
requirement and implementing robust recognition using videos having reduced
data rates can achieve efficiency in storing and transmitting video data.
Three-dimensional video scalability, which refers to the possibility of
reducing spatial, temporal, and quality resolutions of videos, is an effective
way for flexible representation and management of video data. In this paper, we
investigate the impact of the video scalability on multi-view activity
recognition. We employ both a spatiotemporal feature extraction-based method
and a deep learning-based method using convolutional and recurrent neural
networks. The recognition performance of the two methods is examined, along
with in-depth analysis regarding how their performance vary with respect to
various scalability combinations. In particular, we demonstrate that the deep
learning-based method can achieve significantly improved robustness in
comparison to the feature-based method. Furthermore, we investigate optimal
scalability combinations with respect to bitrate in order to provide useful
guidelines for an optimal operation policy in resource-constrained activity
recognition systems.
