Access to large image volumes through camera traps and crowdsourcing provides novel possibilities for animal monitoring and conservation. It calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. Most existing re-identification methods rely on either hand-crafted local features or end-to-end learning of fur pattern similarity. The former does not need labeled training data, while the latter, although very data-hungry typically outperforms the former when enough training data is available. We propose a novel re-identification pipeline that combines the strengths of both approaches by utilizing modern learnable local features and feature aggregation. This creates representative pattern feature embeddings that provide high re-identification accuracy while allowing us to apply the method to small datasets by using pre-trained feature descriptors. We report a comprehensive comparison of different modern local features and demonstrate the advantages of the proposed pipeline on two very different species.

通过相机陷阱和众包获取大量图像为动物监测和保护提供了新的可能性。它需要自动分析方法，特别是在从图像中重新识别个体动物时。大多数现有的重新识别方法依赖于手工制作的局部特征或毛皮图案相似性的端到端学习。前者不需要标记的训练数据，而后者虽然非常需要数据，但在有足够的训练数据可用时通常优于前者。我们提出了一种新颖的重新识别管道，通过利用现代可学习的局部特征和特征聚合，结合了两种方法的优点。这创建了代表性的模式特征嵌入，提供了较高的重新识别精度，同时允许我们通过使用预先训练的特征描述符将该方法应用于小型数据集。我们报告了不同现代当地特征的全面比较，并展示了所提出的管道在两个截然不同的物种上的优势。