## 2 Related Work  
Human pose estimation, which can also be called human keypoint detection, is a relatively basic task compared to other fields in machine vision, such as target detection, image segmentation, image generation, image generation, and face classification recognition, whose main purpose is to estimate the human pose by correctly connecting the detected human joints in the picture [27; 28]. According to whether it contains three-dimensional depth information, human keypoint detection algorithms can be divided into 2D keypoint detection and 3D keypoint detection, and in 2D and 3D keypoint detection can be divided into single and multi-person keypoint detection. In 2016, [PERSON] et al. merged convolutional networks into a pose machine to learn image features and image-dependent spatial models for pose estimation [29], and [PERSON] et al. proposed hourglass-type convolutional networks [30]. Both methods perform the process of detecting people first and then key points for each person. In 2017, a researcher proposed the OpenPose measure, which first detects all keypoints in an image and then associates keypoints in groups by affinity to obtain the corresponding individual person. In 2019, a researcher proposed an improved version of OpenPose, proposing a real-time method for detecting 2D poses of multiple people in an image, which learns how to associate body parts with individuals in the image. OpenPose is applicable to both single- and multi-person scenes and has both better robustness and real-time performance. Compared with 2D skeleton points, 3D skeleton points contain richer skeleton features, such as stereo angle information between joints and depth information around bones. There are three main methods to obtain 3D skeletal points. The first one is to obtain the (x, y, z) coordinates of the joints by the subject wearing a motion tracking sensor. This method is accurate and robust in estimating results, and does not introduce estimation errors due to background occlusion. However, the data acquisition process is tedious, and the sensor equipment is expensive. The second one uses a depth camera and a color camera with a skeletal point estimation algorithm for estimation [31], which is more accurate and easier to use. However, it is sensitive to occlusion and requires the use of algorithms to process the occluded points that are incorrectly predicted, and the portability and higher price affect the large-scale use of depth cameras. The third one is RGB video-based pose estimation, which only needs a general camera to obtain 3D skeletal points, the method is more suitable for large-scale use and promotion, but the accuracy rate is currently inferior to the second method because of the lack of accurate depth information. The RGB video-based human pose estimation algorithm is a hot topic at present and has the potential for large-scale application, so the third method is used in this thesis to extract 3D skeletal points. Based on RGB video 3D key point extraction, the early method of convolutional neural network was used to estimate 3D pose directly from RGB images, and then with the development of deep learning technology, many methods for 3D pose estimation were generated. 2019 [PERSON] et al. proposed a full convolutional model for 3D human pose prediction [32], which only needs to do temporal convolution of 2D key points to achieve human 3D keypoint prediction. [PERSON] et al. proposed PoseDRL [33], a trainable active pose estimation architecture based on deep reinforcement learning, which has high performance for pose estimation in multi-person complex scenes. The analyzed and compared VideoPose3D model can achieve high accuracy with fewer parameters and can be trained and inferred quickly. Human keypoints are the most important information when describing human pose and detecting human movement.