Action recognition by learning temporal slowness invariant features

Deep learning approaches emphasized on learning spatio-temporal features for action recognition. Different to previous works, we separate the spatio-temporal feature learning unity into the spatial feature learning and the spatial/temporal feature pooling procedures. Using the temporal slowness regularized independent subspace analysis network, we learn invariant spatial features from sampled video cubes. To be robust to the cluttered backgrounds, we incorporate the denoising criterion to our network. The local spatio-temporal features are obtained by pooling features from the spatial and the temporal aspects. The key points are that we learn spatial features from video cubes and pool features from spatial feature sequences. We evaluate the learned local spatio-temporal features on three benchmark action datasets. Extensive experiments demonstrate the effectiveness of the novel feature learning architecture.