Multi-views Embedding for Cattle Re-identification
This addresses animal identification for agricultural or monitoring applications, but it is incremental as it adapts existing methods to a new domain.
The paper tackled cattle re-identification using deep CNNs, showing it is distinct from human re-identification and remains unsolved, with results including comparisons to baselines and an ablation study.
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.