Abstract
Neuroscience has traditionally relied on manually observing lab animals in controlled environments. Researchers usually record animals behaving in free or restrained manner and then annotate the data manually. The manual annotation is not desirable for three reasons; one, it is time consuming, two, it is prone to human errors and three, no two human annotators will 100\% agree on annotation, so it is not reproducible. Consequently, automated annotation of such data has gained traction because it is efficient and replicable. Usually, the automatic annotation of neuroscience data relies on computer vision and machine leaning techniques. In this article, we have covered most of the approaches taken by researchers for locomotion and gesture tracking of lab animals. We have divided these papers in categories based upon the hardware they use and the software approach they take. We also have summarized their strengths and weaknesses.
Abstract (translated)
神经科学传统上依赖于在受控环境中人工观察实验动物。研究人员通常记录动物的自由或约束行为，然后手动标注数据。人工注释不可取的原因有三个：一是耗时，二是容易出现人为错误；三是没有两个人工注释员100%同意注释，因此不可复制。因此，这些数据的自动注释由于其有效性和可复制性而受到了广泛的关注。通常，神经科学数据的自动注释依赖于计算机视觉和机器学习技术。在本文中，我们介绍了研究人员对实验动物的运动和姿态跟踪所采取的大部分方法。我们根据他们使用的硬件和他们采用的软件方法将这些论文分为不同的类别。我们还总结了它们的优点和缺点。
URL
https://arxiv.org/abs/1903.10422
