  The video and action classification have extremely evolved by deep neural
networks specially with two stream CNN using RGB and optical flow as inputs and
they present outstanding performance in terms of video analysis. One of the
shortcoming of these methods is handling motion information extraction which is
done out side of the CNNs and relatively time consuming also on GPUs. So
proposing end-to-end methods which are exploring to learn motion
representation, like 3D-CNN can achieve faster and accurate performance. We
present some novel deep CNNs using 3D architecture to model actions and motion
representation in an efficient way to be accurate and also as fast as
real-time. Our new networks learn distinctive models to combine deep motion
features into appearance model via learning optical flow features inside the
network.
