Human Body Full-body Motion Gesture Image Feature Capture in Mobile Sensor Networks

To solve the problems of poor estimation of full-body shape and inaccurate capture results in human motion capture in mobile sensor networks, a method of capturing image features of human full-body motion posture in mobile sensor networks is studied. The method uses Markov random fields to cooperate with sensors to extract human full-body motion foreground images and combines guided filtering to enhance the extraction effect of foreground images. Based on the foreground images, a human tree-structured model is established to simulate the actions of human movements. The extracted foreground images are used as input to the convolutional neural network to extract edge features and spatio-temporal features of human motion posture. After fusion, a human motion posture feature matrix is constructed. Based on the least squares method, a strong regression mapping model is constructed. According to the structure of the human tree model, multi-dimensional iterative mapping is performed from top to bottom between the human motion posture feature matrix and the human tree model. The joint positions corresponding to the human motion posture feature matrix in the human tree model are calculated, and the two-dimensional position information of all joint points of the moving human body is obtained. The capture of human full-body motion posture in mobile networks is completed. Experimental data show that the method has clear foreground image extraction, can effectively obtain human motion features, and has accurate capture results of human full-body motion posture.