Human Behavior Recognition Algorithm Based on HD-C3D Model

To address the problems of low recognition accuracy and long training time of the original C3D (Convolutional 3D) model, this paper proposes a modified method to improve its framework. Firstly, the Relu activation function in the hidden layer is replaced by the Hardswish function to allow more neurons to participate in parameter updating and to alleviate the problem of slow gradient convergence. Secondly, the dataset was optimised using the background difference method and the image scaling improvement respectively, and the optimised dataset was used for model training. The image scaling improvement combined with the activation function improvement results in a better HDs-C3D (Hardswish Data scaling - Convolutional 3D) model. Its accuracy on the training dataset reached 89.1%; meanwhile, the training time per round was reduced by about 25% when trained in the experimental environment of this paper.