###### Abstract
The major contributions of this study are as follows. * First, this paper suggests a dog behavior dataset called the YouTube Companion Dog's Seven Behaviors Dataset (Youtube-C7B). We collected videos containing 2D RGB image-based behavior data of dogs (French Bulldogs, Siberian Huskies, and Retrievers) from the YouTube platform. The proposed Youtube-C7B helps the behavioral modeling of dogs become more natural by collecting videos of dog behaviors in real-world environments, unlike the conventional datasets that have collected behavior data through sensors in limited spaces. Furthermore, while the conventional datasets are without labels, the dataset proposed in this study was built by labeling behaviors suitable for deep learning. Finally, in contrast to the conventional datasets, the proposed dataset contains not only normal behaviors, but also convulsing, which is an abnormal behavior, and can be used in various applications for detecting the abnormal behaviors of dogs and responding to emergency situations. * Second, in this paper, to identify the postures of dogs, a Tightly Coupled RGB Time Tensor Network is proposed, which is an RGB-based data representation method that contains the correlations among the x-, y-, and z-axes, which change as time passes. A good representation of the changing patterns of x, y, and z over time and their relationships help to achieve a modeling with a great distance between each behavioral feature. This leads to an improved accuracy of the dog pose identification. For example, because convulsing results in more shaking on the x-, y-, and z-axes compared to standing, the patterns that change over time and their relationships should be properly represented to effectively model the dog's behaviors. For this reason, we encode the three components (x-, y-, and z-axes) of each 3D joint position into the 3D components (red, green, and blue channels) of the RGB images and then fuse these colors. When the colors are fused in this way, it can be seen as containing the correlations of the x-, y-, and z-axes because even after fusing, it is possible to infer the pre-fusion behavior information, unlike when they were represented by vectors. Furthermore, it helps the deep learning model decode meaningful information to better identify the dog's behaviors. * Third, unlike conventional methods, the proposed method visualizes the filters of Conv-layer in analyzable feature maps. Thus, the patterns that the CNN memorizes for predictions can be understood. By displaying the joints extracted from the visualized feature maps based on a histogram, it is possible to know which joint part of the input image was mainly learned to make decisions. The remainder of this paper is organized as follows. First, Section 2 introduces the trends of prior studies, and Section 3 introduces a video-based dog behavior dataset and an algorithm for the pose classification of dog behaviors using an RGB color space-based color mixing method. Section 3 also describes an algorithm that analyzes the factors affecting the derived results of the CNN model based on the analyzable feature maps. Next, Section 4 describes the experimental environment and method. Finally, Section 5 provides some concluding remarks and describes areas of future study.