Driver distraction detection method

The invention discloses a driver distraction detection method. Converting each frame of driver image into a grayscale image; the method comprises the following steps: firstly, extracting gray scale images corresponding to training samples, performing normalization processing and preprocessing in sequence, inputting one of the training samples into an initialized convolutional neural network, performing batch regularization processing on HOG features extracted from the gray scale images corresponding to the training samples, and then performing full connection layer connection to obtain HOG feature vectors; and finally, carrying out global mean pooling on an output result of each convolution layer to obtain a total feature vector composed of the feature vector and the HOG feature vector, and carrying out full connection layer and Softmax classification in the convolutional neural network in sequence to obtain a global mean pooling result of each convolution layer. Obtaining the actual action category of the dr