# Lightweight Stacked Hourglass Network for Human Pose Estimation
## 1 Introduction  
Human pose estimation is a fundamental method for detecting human behavior, and it is applied in virtual cinematography using computer graphics, human behavior recognition, and building security systems. Joint position varies greatly depending on a variety of factors, such as camera angle, clothing, and context. The traditional method estimates or tracks the human pose using additional equipment, such as depth sensors. However, with the advent of convolutional neural networks (CNNs), it is possible to efficiently infer the entire spatial feature from a single image without the need for additional equipment. Accordingly, many studies on human pose estimation using convolutional neural networks are currently underway, and these have achieved great results in terms of their accuracy [1; 2; 3]. The stacked hourglass network [2] is one of the best-known methods for resolving performance problems in human pose estimation. It has a stacked structure of hourglass modules composed of residual blocks [4]. Since the hourglass network performs promisingly in resolving the human pose estimation problem, a number of studies have used it as a backbone or modified the original hourglass network to improve performance [5; 6; 7; 8; 9; 10]. [PERSON] et al. [11] developed a stacked hourglassdesign and inception-resnet module with encoded external features for human pose estimation. [PERSON] et al. [12] introduced the multiscale supervision network with a multiscale regression network using the stacked hourglass module to increase robustness in keypoint localization for complex background and occlusions. [PERSON] et al. [13] suggested a method to overcome information loss in a repetitive cycle of down-sampling and up-sampling in a feature map. They used a dilated convolution and skip connections applied to a stacked hourglass network to optimize performance while adding extra subnetworks and large parameter sizes. However, the residual block used in the hourglass network consists of a relatively small kernel with a fixed size. This may not be conducive to extracting the relationship between joints in the entire human body, and performance may significantly deteriorate depending on the size of the person in the input image. Additionally, since the network is stacked, very large memory capacity and computational powers are required. In this paper, we focus on how to reduce the parameter size and achieve the best performance, while others [5; 6; 7; 8; 9; 10; 11; 12; 13; 14] deploy an extra subnetwork or layers to the stacked hourglass network. The purpose of this paper is to illustrate an optimized hourglass network with minimized parameter size without sacrificing the quality of the network. Previous studies investigating human pose estimation [2; 6; 15] have confirmed that the size of the convolutional receptive field is a major factor in understanding the whole human body. If the receptive field is too small, it is difficult for the network to understand the relationship between each joint. Conversely, if it is too large, information that is not relevant to pose estimation is used for calculation, leading to impaired performance. In addition, if the convolution operation using a large kernel size is used several times to increase the receptive field, the size of the network may be too large. The goal of this study is to improve the efficiency of the stacked hourglass network [2] in human pose estimation. We propose a method for designing an efficient hourglass network that is lightweight and greatly reduces the number of network parameters. We also propose a residual block that, by expanding the convolutional receptive field of the residual block, enables performance to be maintained on a multiscale basis through multidilation. To verify the performance, we used the MPII dataset [16] and Leeds Sports Poses (LSP) dataset [17], which are widely used human pose estimation datasets, and demonstrated the effectiveness of our approach through various experiments.