# Key Points' Location in Infrared Images of the Human Body Based on Mscf-ResNet
## 1 Introduction
This method learns the location characteristics of key points through a variety of resolution heat maps and obtains the spatial position relationship of key points by means of multi-scale information. [PERSON] et al. [21] proposed a pyramid residual module, which is a multi-branch structure module; for the same input, it can obtain output features of different resolutions. In addition, [PERSON] et al. [22] proposed to use iterative error feedback to locate key points and gradually refine the prediction results. [PERSON] et al. [23] proposed to use deep consensus voting to locate key points, and they also obtained good results. There are two ways to locate the key points of multiple people: top-down and bottom-up methods. The bottom-up method consists of directly predicting all the key points of everyone in the image, and then assigning and connecting the key points according to  
Figure 1: Infrared images of breasts. (**a**) Infrared image of normal leg; (**b**) Infrared image of diseased leg. certain rules. DeepCut [24] uses a CNN to extract body part candidates. Each candidate area corresponds to a joint point. Each joint point is used as a node in the graph, and all nodes form a connection graph. The correlation between the nodes is used as the weight between the nodes in the graph, the joint points belonging to the same person are classified into one category, and each person is regarded as a separate category. This method can solve the problem of multi-person pose estimation, but, because of the use of an adaptive fast R-CNN [25] and integer linear programming, the computational complexity is very large. Deepercut [26] improves on Deepcut. This method uses a residual network to extract body part information and obtains results of higher accuracy. At the same time, it uses image-conditioned pairwise terms to compress candidate region nodes to a certain number of nodes. Aiming at the problem of key point matching and connection, semantic part segmentation [27] uses a segmentation method to provide information for key point connections. This method divides the human body into six parts to provide the key points and the specific part of the human body's subordinate relationship. The partial affinity fields [28] not only carry the affiliation information between key points and body parts, but also the direction information of each pixel. The top-down method involves first accurately locating each pedestrian, and then locating each person's key points. In order to accurately detect the person in the image, most methods use the current state-of-the-art (SOTA) detection method. For example, the method proposed by [PERSON] et al. [29] used the SOTA detection method. This method predicts the offset of the heat map and the heat map relative to the true value at the same time, and then the offset is applied to the heat map to obtain the final key point prediction result. The SSD network proposed by [PERSON] et al. [30] first detects the person in the image, and then also uses the SOTA single-person state estimation model to obtain the key point's position. The mask R-CNN method proposed by [PERSON] et al. [31] first predicts the bounding box of the human body, and then crops the feature map based on the bounding box, which is used for key point detection. [PERSON] et al. [32] proposed a new network structure called a cascaded pyramid network (CPN), which includes a global network and a refined network. The global network roughly detects all the key points, and the refined network specifically detects some key points that are difficult to identify. The rest of the paper is organized as follows. Section 2 presents a brief review of the residual neural network model; Section 3 describes the proposed Mscf-ResNet module; Section 4 outlines the key point positioning experiments and analyzes the positioning results; and Section 5 summarizes the proposed method.