Technology has an important part to play in wildlife and ecosystem conservation, and can vastly reduce time and effort spent in the associated tasks. Deep learning methods for computer vision in particular show good performance on a variety of tasks; animal detection and classification using deep learning networks are widely used to assist ecological studies. A related challenge is tracking animal movement over multiple cameras. For effective animal movement tracking, it is necessary to distinguish between individuals of the same species to correctly identify an individual moving between two cameras. Such problems could potentially be solved through animal re-identification methods. In this paper, the applicability of existing animal re-identification techniques for fully automated individual animal tracking in a cross-camera setup is explored. Recent developments in animal re-identification in the context of open-set recognition of individuals, and the extension of these systems to multiple species is examined. Some of the best performing human re-identification and object tracking systems are also reviewed in view of extending ideas within them to individual animal tracking. The survey concludes by presenting common trends in re-identification methods, lists a few challenges in the domain and recommends possible solutions.

技术在野生动植物和生态系统保护中发挥着重要作用，并且可以大大减少在相关任务中花费的时间和精力。尤其是计算机视觉的深度学习方法在各种任务上表现出良好的性能；使用深度学习网络对动物进行检测和分类被广泛用于协助生态研究。一个相关的挑战是在多个摄像机上跟踪动物的运动。为了进行有效的动物运动跟踪，有必要在同一物种的个体之间进行区分，以正确识别在两个摄像头之间运动的个体。这些问题可以通过动物重新识别方法来解决。在本文中，探讨了现有动物重新识别技术在跨相机设置中用于全自动个体动物追踪的适用性。在对个体进行开放式识别的背景下，对动物重新识别的最新进展进行了研究，并将这些系统扩展到多个物种。还考虑了一些性能最好的人类重新识别和物体追踪系统，以将其中的思想扩展到个体动物追踪。该调查总结了重新识别方法的共同趋势，列出了该领域的一些挑战并提出了可能的解决方案。