# Key Points' Location in Infrared Images of the Human Body Based on Mscf-ResNet
## 1 Introduction  
Infrared imaging technology has the advantages of night vision, good concealment, strong transmission ability, and immunity from electromagnetic wave interference [1]. In addition to military applications, infrared imaging technology is also widely used in many civilian fields, such as industrial monitoring [2], medical treatment [3], remote sensing [4], and night vision [5]. In China, infrared images are gradually being combined with traditional Chinese medicine, playing a huge role in medical diagnosis [6, 7]. As shown in Figure 1, for normal legs, the temperature distribution of the legs is basically symmetrical, while for diseased legs, the temperature distribution of the legs is asymmetrical [3]. Key point detection in human body infrared images is an important research direction in relation to human body pose estimation, and it is widely used in various fields of computer vision. Changes in human body posture caused by body bending or deformation make it difficult to locate key points. However, in recent years, with the development of convolutional neural networks (CNNs), the positioning of key points of the human body has become more and more accurate. Human body key point positioning consists of locating the key parts of the human body in pictures or videos, such as the shoulders, elbows, wrists, and other areas. Two-dimensional key point positioning is used to estimate the two-dimensional coordinate information of key points from the image [8], and three-dimensional key point positioning is used to estimate three-dimensional coordinate information [9]. In this paper, 2D human body key point positioning is studied. The traditional, classical methods include tree models [10; 11], random forest models [12; 13], random field models [14], dependency graph models [15], and so on. These methods mainly use graphical structures to represent human body parts and predict the location of key points based on hand-made features. Although these methods are relatively simple and easy to implement, the positioning accuracy of these methods is not high owing to the changeability of human posture. The use of deep neural networks for key point positioning greatly improves the accuracy of the positioning and has gradually become the mainstream method for key point positioning. According to different situations, it can be divided into single-person key point positioning and multi-person key point positioning. Single-person key point positioning can only handle one person in the center of the picture. [PERSON] et al. [16] introduced the use of a CNN into key point positioning for the first time. This method uses cascaded neural networks to regress coordinate points. However, owing to the changeability of attitudes, this method has great limitations. [PERSON] et al. [17] proposed to use a CNN and a graph model to predict a heat map of the key points, and then find the local maximum in the heat map to obtain the location of the key points. However, this method does not take into consideration the structural information between each key point, and the performance is not good when the characters are partially occluded, so its robustness is poor. In order to overcome the above shortcomings, later neural networks were designed to be more complex. [PERSON] et al. [18] proposed that, by increasing the receptive field of the neural network, the positional relationship in the image can be better obtained. [PERSON] et al. [19] proposed convolutional pose machines (CPMs) based on a multi-stage detection strategy, which first obtains some rough results, and then continuously refines the results in subsequent stages. In order to avoid the disappearance of the gradient caused by the network being too deep, CPM uses intermediate supervision to calculate the loss in the prediction results of each stage. [PERSON] et al. [20] proposed a U-shaped neural network with multiple hourglass modules stacked. In order to prevent over-fitting caused by the network being too deep, the network model uses a residual module.