Human action recognition based on convolutional neural network
Yingzi Wei, Le Jiang · 2021
In the field of human action recognition, a good action classification algorithm has always been the key to measure the accuracy of action recognition. Since the trajectory of human targets is often complex, the recognition of RGB color images is restricted by many influencing factors, such as light intensity, complex background and angle. In this paper, kinect equipment is used to collect depth image data, and the image is generated into bone data. The Alexnet model in convolutional neural network is used to train and classify the data, which effectively improves the accuracy of action recognition.