Wildlife monitoring is crucial to nature conservation and has been done by manual observations from motion-triggered camera traps deployed in the field. Widespread adoption of such in-situ sensors has resulted in unprecedented data volumes being collected over the last decade. A significant challenge exists to process and reliably identify what is in these images efficiently. Advances in computer vision are poised to provide effective solutions with custom AI models built to automatically identify images of interest and label the species in them. Here we outline the data unification effort for the Wildlife Insights platform from various conservation partners, and the challenges involved. Then we present an initial deep convolutional neural network model, trained on 2.9M images across 465 fine-grained species, with a goal to reduce the load on human experts to classify species in images manually. The long-term goal is to enable scientists to make conservation recommendations from near real-time analysis of species abundance and population health.

中文翻译：

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用于细粒度视觉物种分类的统一数据

野生动物监测对于自然保护至关重要，并且已经通过部署在现场的运动触发相机陷阱的手动观察来完成。在过去十年中，此类原位传感器的广泛采用导致收集了前所未有的数据量。有效地处理和可靠地识别这些图像中的内容是一项重大挑战。计算机视觉的进步有望通过定制的 AI 模型提供有效的解决方案，这些模型可以自动识别感兴趣的图像并标记其中的物种。在这里，我们概述了来自各个保护合作伙伴的 Wildlife Insights 平台的数据统一工作，以及所涉及的挑战。然后我们提出了一个初始的深度卷积神经网络模型，在 465 个细粒度物种的 290 万张图像上训练，目的是减少人类专家手动对图像中的物种进行分类的负担。长期目标是使科学家能够通过近乎实时的物种丰度和种群健康分析提出保护建议。