Human Action Recognition Based on Image Coding and CNN

Abstract

In human action recognition, the way of collecting action data through video or photos is easily affected by factors such as perspective and light, and it is not easy to describe and extract features. To solve this problem, we researched human skeletal joint data and the use of the convolutional neural network (CNN). The joint data was converted into a PNG image by image coding. In addition, we proposed 3 descriptions of data arrangement order for grayscale image coding. Combined with 4 coding methods and RGB image coding, the coding scheme was expanded to 16 kinds, and used a CNN model with 9 layers structure to conduct comparative experiments on 16 kinds of coding schemes. Then, the influence of data arrangement order and coding methods was discussed based on action recognition results. The experimental results show that the “Zhi” font coding method under the data arrangement order Case 2 is easier to classify actions, and the accuracy of the test set is 96 %.