  In this paper, a novel video classification method is presented that aims to
recognize different categories of third-person videos efficiently. Our
motivation is to achieve a light model that could be trained with insufficient
training data. With this intuition, the processing of the 3-dimensional video
input is broken to 1D in temporal dimension on top of the 2D in spatial. The
processes related to 2D spatial frames are being done by utilizing pre-trained
networks with no training phase. The only step which involves training is to
classify the 1D time series resulted from the description of the 2D signals. As
a matter of fact, optical flow images are first calculated from consecutive
frames and described by pre-trained CNN networks. Their dimension is then
reduced using PCA. By stacking the description vectors beside each other, a
multi-channel time series is created for each video. Each channel of the time
series represents a specific feature and follows it over time. The main focus
of the proposed method is to classify the obtained time series effectively.
Towards this, the idea is to let the machine learn temporal features. This is
done by training a multi-channel one dimensional Convolutional Neural Network
(1D-CNN). The 1D-CNN learns the features along the only temporal dimension.
Hence, the number of training parameters decreases significantly which would
result in the trainability of the method on even smaller datasets. It is
illustrated that the proposed method could reach the state-of-the-art results
on two public datasets UCF11, jHMDB and competitive results on HMDB51.
