Construction and evaluation of the human behavior recognition model in kinematics under deep learning

To explore the construction and evaluation of the human behavior recognition model in kinematics by deep learning, the convolution neural network (CNN) in the field of deep learning was applied to build the CNN human behavior recognition algorithm model. The image data were collected from the KTH and Weizmann datasets and trained; then, the proposed algorithm was simulated by the TensorFlow platform. The results suggested that in the analysis of recognition effect of kinematic description in two datasets, the accuracy of the histogram of optical flow orientation (HOF) method was the worst in both KTH and Weizmann datasets, while the accuracy of the constructed Visual Geometry Group-16 (VGG-16) algorithm model was the highest. In the analysis of the accuracy of the KTH dataset, the boxing action had the highest accuracy of recognition, and the running action had the lowest accuracy of recognition. The average recognition value of all kinds of actions was 91.93%. In the precision analysis of the Weizmann dataset, the bending and hand waving actions had the most accurate recognition rate, while the running action had the lowest recognition rate.