Learning Representative Temporal Features for Action Recognition

In this paper, a novel video classification methodology is presented that aims to recognize different categories of third-person videos efficiently. The idea is to keep track of motion in videos by following optical flow elements over time. To classify the resulted motion time series efficiently, the idea is letting the machine to learn temporal features along the time dimension. This is done by training a multi-channel one dimensional Convolutional Neural Network (1D-CNN). Since CNNs represent the input data hierarchically, high level features are obtained by further processing of features in lower level layers. As a result, in the case of time series, long-term temporal features are extracted from short-term ones. Besides, the superiority of the proposed method over most of the deep-learning based approaches is that we only try to learn representative temporal features along the time dimension. This reduces the number of learning parameters significantly which results in trainability of our method on even smaller datasets. It is illustrated that the proposed method could reach state-of-the-art results on two public datasets UCF11 and jHMDB with the aid of a more efficient feature vector representation.