  Information of time differentiation is extremely important cue for a motion
representation. We have applied first-order differential velocity from a
positional information, moreover we believe that second-order differential
acceleration is also a significant feature in a motion representation. However,
an acceleration image based on a typical optical flow includes motion noises.
We have not employed the acceleration image because the noises are too strong
to catch an effective motion feature in an image sequence. On one hand, the
recent convolutional neural networks (CNN) are robust against input noises. In
this paper, we employ acceleration-stream in addition to the spatial- and
temporal-stream based on the two-stream CNN. We clearly show the effectiveness
of adding the acceleration stream to the two-stream CNN.
