Abstract
Quantifying rat behavior through video surveillance is crucial for medicine, neuroscience, and other fields. In this paper, we focus on the challenging problem of estimating landmark points, such as the rat's eyes and joints, only with image processing and quantify the motion behavior of the rat. Firstly, we placed the rat on a special running machine and used a high frame rate camera to capture its motion. Secondly, we designed the cascade convolution network (CCN) and cascade hourglass network (CHN), which are two structures to extract features of the images. Three coordinate calculation methods—fully connected regression (FCR), heatmap maximum position (HMP), and heatmap integral regression (HIR)—were used to locate the coordinates of the landmark points. Thirdly, through a strict normalized evaluation criterion, we analyzed the accuracy of the different structures and coordinate calculation methods for rat landmark point estimation in various feature map sizes. The results demonstrated that the CCN structure with the HIR method achieved the highest estimation accuracy of 75%, which is sufficient to accurately track and quantify rat joint motion.
Highlights
Rats, which are genetically similar to humans with low feeding costs, have been widely used in research of neuroscience, medicine, the social sciences, and other fields (Scaglione et al, 2014; Chan et al, 2017; Zhang et al, 2017)
Combining the neural network structures and coordinate calculation methods, it is demonstrated that using the cascade convolution network (CCN) structure and the heatmap integral regression (HIR) method constitutes the optimal approach for the task of rat landmark point estimation and achieves 75% accuracy in the test set
We focus on the estimation of rat landmark points and the quantification of joint motion without invasive sensors or markers
Summary
Rats, which are genetically similar to humans with low feeding costs, have been widely used in research of neuroscience, medicine, the social sciences, and other fields (Scaglione et al, 2014; Chan et al, 2017; Zhang et al, 2017). Invasive sensors or markers are used to acquire more robust behavior observations for neuroscience or social science research (Weissbrod et al, 2013; Wenger et al, 2014) These methods, necessitate complex surgery or special markers to achieve the desired results (Burgos-Artizzu et al, 2012; Ohayon et al, 2013; Eftaxiopoulou et al, 2014; Maghsoudi et al, 2017). In the past 2 years, the observation of rat behavior based on deep neural networks has greatly improved the robustness of the observation results without the need for invasive sensors or markers (Mathis et al, 2018; Jin and Duan, 2019). In this paper, we focus on rat landmark estimation to quantify joint motion and conduct locomotor kinematic analysis
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