Visualizing and quantifying movement from pre-recorded

When studying animal behaviour within an open environment, movement-related data are often important for behavioural analyses. Therefore, simple and efficient techniques are needed to present and analyze the data of such movements. However, it is challenging to present both spatial and temporal information of movements within a two-dimensional image representation. To address this challenge, we developed the spectral time-lapse (STL) algorithm that re-codes an animal’s position at every time point with a time-specific color, and overlays it with a reference frame of the video, to produce a summary image. We additionally incorporated automated motion tracking, such that the animal’s position can be extracted and summary statistics such as path length and duration can be calculated, as well as instantaneous velocity and acceleration. Here we describe the STL algorithm and offer a freely available MATLAB toolbox that implements the algorithm and allows for a large degree of end-user control and flexibility. This article is included in the gateway. INCF Christopher R. Madan ( ) Corresponding author: [email protected] The authors declare that the research was conducted in the absence of any commercial or financial relationships that could Competing interests: be construed as a potential conflict of interest. Madan CR and Spetch ML. How to cite this article: Visualizing and quantifying movement from pre-recorded videos: The spectral 2014, :19 (doi: ) time-lapse (STL) algorithm [version 1; referees: 2 approved] F1000Research 3 10.12688/f1000research.3-19.v1 © 2014 Madan CR and Spetch ML. This is an open access article distributed under the terms of the Copyright: Creative Commons Attribution , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated Licence with the article are available under the terms of the (CC0 1.0 Public domain dedication). Creative Commons Zero "No rights reserved" data waiver This research was partly funded by a Discovery grant and a Canada Graduate Scholarship, both from the Natural Science and Grant information: Engineering Research Council of Canada, held by MLS and CRM, respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 21 Jan 2014, :19 (doi: ) First published: 3 10.12688/f1000research.3-19.v1 Referee Status: