Abstract
Automation of complex social behavior analysis of experimental animals would allow for faster, more accurate and reliable research results in many biological, pharmacological, and medical fields. However, there are behaviors that are not only difficult to detect for the computer, but also for the human observer. Here, we present an analysis of the method for identifying aggressive behavior in thermal images by detecting traces of saliva left on the animals’ fur after a bite, nape attack, or grooming. We have checked the detection capabilities using simulations of social test conditions inspired by real observations and measurements. Detection of simulated traces different in size and temperature on single original frame revealed the dependence of the parameters of commonly used corner detectors (R score, ranking) on the parameters of the traces. We have also simulated temperature of saliva changes in time and proved that the detection time does not affect the correctness of the approximation of the observed process. Furthermore, tracking the dynamics of temperature changes of these traces allows to conclude about the exact moment of the aggressive action. In conclusion, the proposed algorithm together with thermal imaging provides additional data necessary to automate the analysis of social behavior in rodents.
Highlights
Laborator animal behavior analysis is an issue of great value in such research branches as medicine, pharmacology, biology, or neuroscience
We introduce a length ratio (LR) which is defined as the ratio of the time period within which the saliva trace was detected to the time period when the saliva trace was simulated: LR =
Score and the ranking differ with respect to groups of three factors for the Harris detector and two factors for the FAST algorithm
Summary
Laborator animal behavior analysis is an issue of great value in such research branches as medicine, pharmacology, biology, or neuroscience. The problem, is the manual scoring, which is error-prone. Due to this limitation, researchers have proposed different solutions for automation of animal behavior analysis. The most common approach is to distinguish behavior on the basis of the mutual position and the body displacement [2,3]. Few studies have been adopting statistical measures for trajectory analysis [5,6,7]. Another solution uses detailed ethograms in combination with multivariate approach known as T-pattern analysis [8]
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