## 2 Materials and Methods
### Data Augmentation  
To ensure effective training of a deep learning model, high-quality data in large quantities are essential. However, the collection and labeling of such large datasets require significant time and labor. Therefore, this study used data augmentation techniques such as AutoAugment and GridMask.  
AutoAugment employs a reinforcement learning algorithm trained on a specific dataset to determine the most effective augmentation policy [19]. There exist 16 types of image augmentation methods: shearX (Y), translateX (Y), rotate, AutoContrast, invert, equalize, solarize, posterize, contrast, color, brightness, sharpness, cutout, and sample pairing. AutoAugment produces 25 pairs of techniques by combining two techniques from this list. The 25 pairs of generated techniques are applied with varying probabilities and intensity levels to introduce diverse modifications to the images, thereby enabling the model to become more robust to variations. This study compared the performances of the augmentation policies trained on the Cifar-10 and ImageNet datasets [20; 21]. After data augmentation, the number of training images was increased by 25\(\times\), from 11,154 to 278,850.  
Data augmentation methods such as cutout, hide-and-seek, and GridMask based on information removal encourage the object detection model to focus on learning from a variety of features of the object rather than relying solely on specific characteristics [22; 23; 24]. This approach not only improves the model's generalization performance but also enhances its robustness in diverse environments.  
Among the data augmentation methods based on information removal, GridMask uniquely deletes rectangular areas uniformly and masks parts of the image to enhance the diversity of the training data. As a result, the model becomes more robust to various modifications and changes present within the dataset. Furthermore, integrating GridMask introduces randomness and unpredictability into the training phase, serving as a preventive measure against overfitting. It ensures the model remains effective when encountering previously unseen data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera** & **Train** & **Validation** & **Test** & **Total** \\ \hline Camera A & 3723 & 1235 & 1230 & 6188 \\ Camera B & 3711 & 1237 & 1236 & 6184 \\ Camera C & 3720 & 1234 & 1223 & 6177 \\ Total & 11,154 & 3706 & 3689 & 18,549 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset configurations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Train** & **Validation** & **Test** & **Total** \\ \hline Resting & 10,567 & 4396 & 3218 & 18,181 \\ Communication & 1186 & 114 & 846 & 2146 \\ Feeding & 851 & 173 & 416 & 1440 \\ Drinking & 299 & 93 & 155 & 547 \\ Eating & 5764 & 2060 & 2797 & 10,591 \\ Total & 18,667 & 6836 & 7432 & 32,905 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of labels for each class.  
In this study, GridMask was applied to 278,850 images that had been augmented using AutoAugment. GridMask was implemented with a 50% probability, ensuring that the augmentation process introduced a balanced mixture of obscured and unobscured images. The combination of these two augmentation techniques increases the diversity of the dataset and, consequently, the robustness and generalization performance of the object detection model.