  The objective of this paper is to evaluate "human action recognition without
human". Motion representation is frequently discussed in human action
recognition. We have examined several sophisticated options, such as dense
trajectories (DT) and the two-stream convolutional neural network (CNN).
However, some features from the background could be too strong, as shown in
some recent studies on human action recognition. Therefore, we considered
whether a background sequence alone can classify human actions in current
large-scale action datasets (e.g., UCF101).
  In this paper, we propose a novel concept for human action analysis that is
named "human action recognition without human". An experiment clearly shows the
effect of a background sequence for understanding an action label.
