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Identification of animals and recognition of their actions in wildlife videos using deep learning techniques
Ecological Informatics ( IF 5.1 ) Pub Date : 2021-01-27 , DOI: 10.1016/j.ecoinf.2021.101215
Frank Schindler , Volker Steinhage

Biodiversity crisis has continued to accelerate. Studying animal distribution, movement and behaviour is of critical importance to address environmental challenges such as spreading of diseases, invasive species, climate and land-use change. Camera traps are an appropriate technique for continuous animal monitoring in an automated 24/7/52 documentation. This study shows a proof-of-concept for an end-to-end pipeline to detect and classify animals and their behaviour in video clips. Video clips are captured with 8 frames per second by camera traps using infrared cameras and infrared flash-lights. The clips show deer, boars, foxes and hares - mostly at night time. Our approach shows an average precision of 63.8% for animal detection and identification. For action recognition the achieved accuracies range between 88.4% and 94.1%.



中文翻译:

使用深度学习技术在野生动物视频中识别动物并识别其行为

生物多样性危机继续加剧。研究动物的分布,运动和行为对于应对环境挑战(如疾病传播,入侵物种,气候和土地利用变化)至关重要。相机陷阱是自动24/7/52文档中用于连续动物监视的适当技术。这项研究显示了用于对视频中的动物及其行为进行检测和分类的端到端管道的概念证明。使用红外摄像机和红外闪光灯的摄像机陷阱以每秒8帧的速度捕获视频剪辑。剪辑显示鹿,野猪,狐狸和野兔-主要是在夜间。我们的方法显示出动物检测和识别的平均精度为63.8%。对于动作识别,获得的准确度在88.4%和94.1%之间。

更新日期:2021-02-07
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