Abstract: With the development of deep learning technology, the accuracy of human posture estimation and tracking based on convolutional neural network has been significantly improved. However, when facing occlusion problems, there are still difficulties in detecting the key points of the human body, low posture tracking accuracy, and slow speed. In this paper, a ybasTrack multi-person pose estimation and tracking model is constructed to address these problems. An improved YOLOv5s network is proposed for target detection; a BCNet segmentation network is used to distinguish between occluded and occluded human bodies and limit the localization area of human body key points. Alphapose-based SPPE is improved to optimize the detection results of human key points. An improved Y-SeqNet network is used for pedestrian re-identification, and the MSIM identity feature matching algorithm is used to match the human body frame, posture, and identity information, achieving human body posture tracking. It is shown from experiment results that the proposed algorithm has better performance in human posture estimation and tracking in occlusion scenes, and the model runs at a faster speed.