  In this paper, a new video classification methodology is proposed which can
be applied in both first and third person videos. The main idea behind the
proposed strategy is to capture complementary information of appearance and
motion efficiently by performing two independent streams on the videos. The
first stream is aimed to capture long-term motions from shorter ones by keeping
track of how elements in optical flow images have changed over time. Optical
flow images are described by pre-trained networks that have been trained on
large scale image datasets. A set of multi-channel time series are obtained by
aligning descriptions beside each other. For extracting motion features from
these time series, PoT representation method plus a novel pooling operator is
followed due to several advantages. The second stream is accomplished to
extract appearance features which are vital in the case of video
classification. The proposed method has been evaluated on both first and
third-person datasets and results present that the proposed methodology reaches
the state of the art successfully.
