As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring.

中文翻译：

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在充满挑战的环境中对被摄像机困住的动物群进行高度精确的社区科学注释


随着相机陷阱的普及和应用，一些分析限制仍然存在，包括处理时间和数据注释的准确性。通常，图像是由相机陷阱记录的，尽管视频越来越多地被收集，尽管它们需要更多的时间进行注释。为了克服图像注释的局限性，相机陷阱研究越来越多地与社区科学 (CS) 平台联系起来。在这里，我们将之前的 CS 图像注释工作扩展到具有挑战性的环境中的相机陷阱视频；茂密的热带森林，由于树冠覆盖很厚，摄像机水平的灌木丛茂密，能见度低且遮挡高。使用为对来自热带非洲的 599 956 个视频剪辑进行分类而建立的 CS 平台 Chimp&See，我们通过将专业生态学家 (PE) 对 13 531 个 1 分钟视频剪辑的分类与 1744 个已注册视频剪辑的输出进行比较来评估注释精度和准确度作为未注册的 Chimp&See 社区科学家。我们考虑了 29 个分类类别，包括 17 个物种和 12 个更高级别的类别，其中表型相似的物种被分组。总体而言，注释精度为 95.4%，当将相似的物种组聚集在一起时，注释精度提高到 98.2%。我们的研究结果证明了社区科学家在甚至具有挑战性的环境中处理相机陷阱视频的能力，并为未来关于动物行为、物种相互作用动态和种群监测的研究带来了巨大的希望。