Pedestrian detection is an important branch of deep learning object detection field, but there are serious occlusion problems in dense scenes, which brings great challenges to pedestrian detection. To alleviate this problem, a task alignment method for target detection and attitude key point detection was proposed on the CenterNet multitask learning model, and the improved model was Center_tood. Firstly, the separation module is proposed. This module separates the original features into the features that pay more attention to each task. On this basis, a task alignment method is proposed: the alignment measurement is designed to constrain the loss, so that the model can optimize towards the direction of multitask alignment to a greater extent on the gradient. At the same time, the consistency constraint is used to make the model learn the common information between different tasks, so as to align the features of different tasks. In the experiment part, CrowdPose data set was used for training and testing. The AP value of the proposed algorithm is 743%, which increases by 115%. The key point AP value of human posture was 558%, which increased by 96%. Experimental results verify the effectiveness of the proposed multitask learning algorithm in pedestrian detection in dense scenes.