Jensen Grady W, van der Smagt Patrick, Heiss Egon, Straka Hans, Kohl Tobias
Graduate School of Systemic Neurosciences (GSN-LMU), Ludwig-Maximilians-University Munich, Munich, Germany.
argmax.ai, Volkswagen Group Machine Learning Research Lab, Munich, Germany.
Front Behav Neurosci. 2020 Aug 3;14:116. doi: 10.3389/fnbeh.2020.00116. eCollection 2020.
Current neuroethological experiments require sophisticated technologies to precisely quantify the behavior of animals. In many studies, solutions for video recording and subsequent tracking of animal behavior form a major bottleneck. Three-dimensional (3D) tracking systems have been available for a few years but are usually very expensive and rarely include very high-speed cameras; access to these systems for research is limited. Additionally, establishing custom-built software is often time consuming - especially for researchers without high-performance programming and computer vision expertise. Here, we present an open-source software framework that allows researchers to utilize low-cost high-speed cameras in their research for a fraction of the cost of commercial systems. This software handles the recording of synchronized high-speed video from multiple cameras, the offline 3D reconstruction of that video, and a viewer for the triangulated data, all functions previously also available as separate applications. It supports researchers with a performance-optimized suite of functions that encompass the entirety of data collection and decreases processing time for high-speed 3D position tracking on a variety of animals, including snakes. Motion capture in snakes can be particularly demanding since a strike can be as short as 50 ms, literally twice as fast as the blink of an eye. This is too fast for faithful recording by most commercial tracking systems and therefore represents a challenging test to our software for quantification of animal behavior. Therefore, we conducted a case study investigating snake strike speed to showcase the use and integration of the software in an existing experimental setup.
当前的神经行为学实验需要复杂的技术来精确量化动物的行为。在许多研究中，用于视频记录以及后续动物行为跟踪的解决方案构成了一个主要瓶颈。三维（3D）跟踪系统已经出现了几年，但通常非常昂贵，而且很少配备超高速摄像机；用于研究的此类系统的使用受限。此外，开发定制软件通常很耗时——尤其是对于没有高性能编程和计算机视觉专业知识的研究人员而言。在此，我们展示了一个开源软件框架，该框架能让研究人员在其研究中使用低成本的超高速摄像机，而成本仅为商业系统的一小部分。该软件可处理来自多个摄像机的同步高速视频的录制、该视频的离线3D重建以及三角测量数据的查看器，所有这些功能以前也可作为单独的应用程序使用。它为研究人员提供了一套经过性能优化的功能套件，涵盖了整个数据收集过程，并减少了对包括蛇在内的多种动物进行高速3D位置跟踪的处理时间。对蛇进行运动捕捉可能特别具有挑战性，因为一次攻击可能短至50毫秒，实际上是眨眼速度的两倍。对于大多数商业跟踪系统来说，这个速度太快而无法进行可靠记录，因此对我们用于量化动物行为的软件来说是一个具有挑战性的测试。因此，我们进行了一项案例研究，调查蛇的攻击速度，以展示该软件在现有实验装置中的使用和集成情况。
