Real-time Multi-person Pose Tracking Method Using Deep Reinforcement Learning

Abstract

To address the problem of low tracking accuracy caused by many recognized objects in the existing methods, we propose a real-time multi-person pose tracking method using deep reinforcement learning. First, the convolutional neural network (CNN) is used to predict the human key points and center vector in grid mode, make the human key points point to the human center according to the center vector, group the human key points according to the distance from the human key points to the human center, complete the multi-person pose estimation, and obtain the human pose sequence diagram. Then, the human pose sequence diagram is input into the deep reinforcement learning network, and the pose label and category label are output by the supervised learning and training stage. The best pose tracking strategy obtained in the reinforcement learning and training stage is applied to online tracking. Finally, CNN is used to predict the rectangular frame position of the pose instead of the target pose, and the tracking is completed when the pose stops. At this time, the rectangular frame position is the result of multi-person pose tracking. The results show that the maximum expected average overlap (EAO) of the proposed method is 0.53. When the root mean square error (RMSE) of the position component threshold reaches 8, the accuracy has been stable at 0.98%. Therefore, the proposed method has high tracking accuracy. In the future, it can be applied to smart home scenarios to realize smart home human pose tracking, effectively identify human dangerous pose and ensure residents’ life safety.