Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

Abstract

Human posture estimation is the basis of many computer vision tasks, such as motion recognition, violence detection and behavior understanding. Therefore, it is of great significance to study the estimation algorithm of human motion posture (HMP). To address the problem of poor estimation effect of traditional HMP estimation algorithm, in this paper, an estimation algorithm for HMP using multi-labeling transfer learning is proposed. First, the original human motion image is labeled by using the multi-label transfer learning, the HMP features are extracted, and the original image classification is completed. Second, a regulator is constructed based on the classification results of the original image, and the regulator is used to adjust the estimation results of HMP based on a convolutional neural networks. Finally, the posture compensation function is used to compensate for the error part to realize the estimation of HMP. In the experiment, the Human3.6m data set and MPII data set were used as the basis for testing. The results show that the proposed algorithm has high correct recognition rate of HMP. The similarity between the posture estimation results, and the target image is 92%-97%. The accuracy of posture estimation is 98.1%. The proposed algorithm can be widely used in many fields, such as human-computer interaction, recognition authentication and intelligent monitoring.

Keywords:
Human motion posture (HMP); Posture estimation; Multi-labeling transfer learning; Image label

HIGHLIGHTS

• This paper addresses the problem that it is difficult to mine and extract the potential features of human motion posture feature vector.

• The posture compensation function is used to compensate the error part to ensure the accuracy of posture estimation results

• The posture compensation function is used to correct and compensate the estimation results and improve the reliability of the estimation results.