## 4 Experiments
### Experiments and Results  
In the performance assessment of the classification model, the performance should be measured by considering not just the ground truths, but also errors, because no perfect answer can be obtained. Therefore, the _accuracy_, _precision_, _recall_, _f1 score_, and error rate (_MSE_) were used as the performance metrics for the dog behavioral pose classification during every experiment. The _MSE_ was used as the error rate because it is calculated by considering the error in the correct answer rate not only for the ground truth, but also for other incorrect answers. The assessment formulas used are shown in Equations (7)-(11). In Equations (7)-(10), _TP_ indicates a true positive, _TN_ is a true negative, _FP_ indicates a false positive, and _FN_ is a false negative. In Equation (11), \(Y_{i}\) represents the real observation value, and \(\hat{Y}_{i}\) represents the predicted value. \[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F1\,score=\frac{2(Recall*Precision)}{Recall+Precision}=\frac{2 TP}{2 TP+FP+ FN} \tag{10}\]  
Figure 6: Structure of compared models: (**a**) LSTM, (**b**) GRU, (**c**) RNN, and (**d**) TRT-Net (our approach). \[MSE=\frac{1}{df_{E}}\sum\bigl{(}Y_{i}-\hat{Y}_{i}\bigr{)}^{2} \tag{11}\]  
**Experiments 1. Performance comparison based on color space. **  
To increase the behavioral pose classification performance of dogs, it is important to maintain the identity of the original data, knowing the relationships between the extracted skeletons and the relationships among the behavior sequences. Furthermore, even for the same data values, the form of the numerical data and the expressed color vary depending on which color space they are based on. Therefore, it is important to know the color space, based upon which the numerical data are represented and learned. In this study, we propose a method for representing the relationships of dog behavior data in RGB color. Herein, we conducted an analysis to identify the color space, for which the dog behavior data show the best behavioral classification performance. Many color spaces such as RGB, YCbCr, and HSV are used in the field of image recognition. In the experiments, we compared the performance between the models in the RGB, HSV, and HSL spaces, which are the most commonly used among the many color spaces in the computer vision field. The RGB color space is based on what was described in Section 3.2.2, and the HSV and HSL color spaces are calculated based on the RGB color space. **RGB\(\rightarrow\)HSV Color Space Conversion. ** Normalized values of RGB are used, and for the method of converting from RGB into HSV, we use a method proposed by [PERSON] et al. [32]. RGB values are converted into HSV values. HSV includes the hue, saturation-chroma, and brightness values. **RGB\(\rightarrow\)HSL Color Space Conversion. ** For the method of converting from RGB to HSL, we use a method proposed by [PERSON] et al. [33]. The RGB values are converted into HSL values. The RGB color model is a color model created mostly for use in systems or hardware, and the HSV and HSL color models are user-oriented color models based on the color perception of humans [33]. As shown in Figure 7, the form of the numerical data and the position of the color are represented differently depending on which color space is used, even if the color is the same. Because the represented brightness is different in each color space, even the same color shows a different value and form in each color space. Furthermore, as shown in Table 5, even for the same behavior data, the value and form, which are represented differently in each color space, have an impact on the image learning and classification results. Figure 7: Expressing the color location and value according to each color model. (**a**) RGB color model, (**b**) HSV color model and (**c**) HSL color model.