![]() ![]() We also incorporate automated tracking of the animal’s path and provide summary statistics (įigure 1B), as well as plotting velocity and acceleration over time (įigure 1C). Specifically, we developed spectral time-lapse (STL) images that code the animal’s position with a time-specific color and overlay them on a frame of the video to produce a summary image (įigure 1A). ![]() Using simple pre-recorded video recordings, we sought to summarize both spatial and temporal information of movements within a two-dimensional image representation. Although some researchers use commercial tracking equipment, movements are sometimes recorded using standard video cameras without markers on the animal and the data are manually scored. A widely-used solution to this problem was introduced three decades ago, with a methods paper describing the use of video recordings to study animal behaviour ( While behaviour can often be summarized through simple measurements (e.g., first target approached within an array, sequence of targets approached, timings of these behaviours), these measures are not always sufficient. Studies of animal behaviour in open environments yield rich datasets. 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. 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. 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. However, it is challenging to present both spatial and temporal information of movements within a two-dimensional image representation. Therefore, simple and efficient techniques are needed to present and analyze the data of such movements. The figure below shows an example of a normal video screenshot next to what the subtracted background space looks like for detecting and tracking objects.When studying animal behaviour within an open environment, movement-related data are often important for behavioural analyses. The path of each detected object is predicted with a simple Kalman filter and if a subsequent object is detected along the predicted track, it is assigned as the same object. An object is detected by first subtracting the background of two frames and if the difference between the two frames contains enough connected pixels, an object is identified. Unlike popular detection algorithms which implement deep learning and extensive amounts of training data to detect objects, Matlab's motion-based algorithm uses only movement.
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