## 1 Introduction  
Research on human pose estimation has progressed considerably through the application of deep learning. Although [PERSON] et al. [1] reported that researchers have traditionally focused on analyzing the pose estimations of human beings, similar research on animals has received increasing attention in recent years. DeepLabCut [2] and DeepPoseKit [3] have developed artificial intelligence (AI) algorithms for the identification of animal joints without the need for body sensors. These algorithms use software packages to save the location of animal joints, train artificial neural networks based on the saved data, and enable artificial neural networks to track the movements of animals. Generally, researchers utilize a pose-estimation method that installs sensors on the animals' bodies. [PERSON] et al. [4], [PERSON] et al. [5], and [PERSON] et al. [6] used inertial measurement units (IMUs) such as gyro and acceleration sensors to identify the conditions and behaviors of dogs. In these studies, the dogs sported wearable devices equipped with the aforementioned sensors, which enabled the researchers to recognize conditions and behaviors that could not be verbally described. However, certain animals might experience pain or discomfort when equipped with these sensors. Furthermore, this approach involves considerable effort. These obstacles hinder the creation of massive datasets of animal joint locations comparable to those available for human joint locations. [PERSON] [7] stated that behavioral disorders in animals such as companion dogs are quite similar to those experienced by humans, and the ratio of dogs suffering from depression is similar to that of humans. Depression is regarded as a type of a mental disorder, which can be classified as eating disorders and behavioral disorders in dogs. [PERSON] [8] studied the effects of stress on the behavior and well-being of companion dogs. It was found that physical and psychological stress factors can lead to dysregulation in companion dogs,which might increase the risk of their physical and behavioral pathological symptoms. From these results, [PERSON] [8] concluded that stress management for companion dogs is an essential area in veterinary medicine. [PERSON] et al. [9] found that dog walking had a crucial impact on stress levels. The Royal Society for the Prevention of Cruelty to Animals (RSPCA) found that the longer the dogs were left alone, the more behaviors related to separation anxiety disorder (SAD) were observed [10]. Accordingly, the present study defined circling as a problematic behavior in dogs and developed a mobile application that identifies the stress levels in dogs by recording their walking times and time spent alone and by analyzing the problematic stress-related behaviors based on circling. To this end, an image processing technique was used that applies a deep learning algorithm to enable the mobile application to recognize the dogs' behaviors without needing to place sensors on their bodies. The developed mobile app alerts dog owners to canines' stress-related behaviors as well as their causes without sensors being placed on the dogs' bodies. This paper's specific technical contribution is two-fold. Firstly, it is difficult to obtain a relevant dataset owing to the limited research on animal-pose estimation; therefore, this study contributed to preparing a dog joint position-based dataset by estimating three-dimensional poses from the multi-view RGB images. Secondly, previously, dogs were required to sport a wearable device with sensors for behavior analysis. However, our proposed approach predicts 3D poses from images showing 2D poses by using the Video-Pose3D model, thus enabling the analysis of their behaviors without attaching any sensors.