DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
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DOI：
10.1038/s41593-018-0209-y
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发表时间：
2018-09-01
影响因子：
25
通讯作者：
Bethge, Matthias
中科院分区：
文献类型：
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作者：
Mathis, Alexander;Mamidanna, Pranav;Bethge, Matthias
量化行为对于神经科学中的许多应用都是至关重要的。摄像技术提供了在不同环境中观察和记录动物行为的简单方法，但提取行为的特定方面以进行进一步分析可能非常耗时。在运动控制研究中，人类或其他动物经常被标记上反光标记，以帮助基于计算机的跟踪，但标记是侵入性的，标记的数量和位置必须事先确定。本文提出了一种基于深度神经网络转移学习的无标记姿态估计方法，该方法使用最少的训练数据即可获得较好的估计结果。我们通过在广泛的行为集合中跟踪多个物种的不同身体部位，展示了这个框架的多功能性。值得注意的是，即使只有少量的帧被标记(类似于200帧)，该算法在测试帧上也获得了与人类准确率相当的优秀跟踪性能。
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (similar to 200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.