抄録
We demonstrate the effectiveness of space-time features incorporated in person re-identification (Re-ID). The space-time representation, improved dense trajectories, effectively extracts multiple features on a large number of trajectories. The experiments show that our proposal outperforms convolution neural network features on the iLIDS-VID dataset. Moreover, we customize the space-time feature for Re-ID. The numbers of temporal accumulations and visual words are highly tuned to classify individuals.
