  People re-identification task has seen enormous improvements in the latest
years, mainly due to the development of better image features extraction from
deep Convolutional Neural Networks (CNN) and the availability of large
datasets. However, little research has been conducted on animal identification
and re-identification, even if this knowledge may be useful in a rich variety
of different scenarios. Here, we tackle cattle re-identification exploiting
deep CNN and show how this task is poorly related with the human one,
presenting unique challenges that makes it far from being solved. We present
various baselines, both based on deep architectures or on standard machine
learning algorithms, and compared them with our solution. Finally, a rich
ablation study has been conducted to further investigate the unique
peculiarities of this task.
