Abstract
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.
Highlights
In most snake studies only one camera or a maximum of two are used to capture such rapid motion, with the one exception of a recent study where multiple cameras with only moderate temporal resolution, were used to investigate locomotor maneuvers (Gart et al, 2019). Software such as DLTdv (Hedrick, 2008), Tracker (Open Source Physics)1, ImageJ (Rasband, 1997-2018), or Didge (Alistair Cullum, Creighton University) have usually been used to process the captured images. These open source solutions are suitable tools to use when capturing with a single camera and with a known distance to the recorded object(s), with an exception for DLTdv as it performs triangulation when combined with calibration information provided by a different software
Using a mirror allows a single camera to perceive multiple views of the snake such as done by Kardong and Bels (1998), but any time a single camera is used to capture three-dimensional (3D) information, the camera must be placed in a setup that is stereotypically well-defined in a way that the distances such as between camera sensors or from the camera sensor to the object are known
The accuracy of the tracking system was determined by using a robotic arm for the generation of a movement of the flat plane through the visual field of the cameras (Figures 1E,F)
Summary
High-speed video recording is a common tool to visualize and subsequently quantify fast behavioral performances such as in snakes (Kardong and Bels, 1998; Young, 2010; Herrel et al, 2011; Penning et al, 2016; Ryerson and Tan, 2017), other fast moving animals (Patek et al, 2004; Tobalske et al, 2007; Seid et al, 2008), or insect flight (e.g. Altshuler et al, 2005; Boeddeker et al, 2010; Low-Cost 3D Motion Capture SystemGeurten et al, 2010; Straw et al, 2011). In most snake studies only one camera or a maximum of two are used to capture such rapid motion, with the one exception of a recent study where multiple cameras with only moderate temporal resolution, were used to investigate locomotor maneuvers (Gart et al, 2019) Software such as DLTdv (Hedrick, 2008), Tracker (Open Source Physics), ImageJ (Rasband, 1997-2018), or Didge (Alistair Cullum, Creighton University) have usually been used to process the captured images. Using a mirror allows a single camera to perceive multiple views of the snake such as done by Kardong and Bels (1998), but any time a single camera is used to capture three-dimensional (3D) information, the camera must be placed in a setup that is stereotypically well-defined in a way that the distances such as between camera sensors or from the camera sensor to the object are known The inflexibility of these well-defined setups can be troublesome for the use in multiple experiments, requires extra expertise, and entails extra costs for building and storage
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