  Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve
state-of-the-art human action recognition results on a number of datasets. This
paper improves their performance by applying rank pooling to each trajectory,
encoding the temporal evolution of deep learning features computed along the
trajectory. This leads to Evolution-Preserving Trajectory (EPT) descriptors, a
novel type of video descriptor that significantly outperforms Trajectory-pooled
Deep-learning Descriptors. EPT descriptors are defined based on dense
trajectories, and they provide complimentary benefits to video descriptors that
are not based on trajectories. In particular, we show that the combination of
EPT descriptors and VideoDarwin leads to state-of-the-art performance on
Hollywood2 and UCF101 datasets.
