公开(公告)号：US11783601B2

公开(公告)日：2023-10-10

申请号：US17043681

申请日：2020-08-18

Applicant: Nanjing University of Science and Technology

Inventor： Yong Qi ,  Yuan Zhuang

IPC: G06F9/00 ,  G08B21/18 ,  G06V20/59 ,  G06V20/40 ,  G06V10/82 ,  G06V10/776 ,  G06V10/77

CPC classification number: G06V20/597 ,  G06V10/7715 ,  G06V10/776 ,  G06V10/82 ,  G06V20/46 ,  G06V20/49 ,  G08B21/18

Abstract: A driver fatigue detection method based on combining a pseudo-three-dimensional (P3D) convolutional neural network (CNN) and an attention mechanism includes: 1) extracting a frame sequence from a video of a driver and processing the frame sequence; 2) performing spatiotemporal feature learning through a P3D convolution module; 3) constructing a P3D-Attention module, and applying attention on channels and a feature map through the attention mechanism; and 4) replacing a 3D global average pooling layer with a 2D global average pooling layer to obtain more expressive features, and performing a classification through a Softmax classification layer. By analyzing the yawning behavior, blinking and head characteristic movements, the yawning behavior is well distinguished from the talking behavior, and it is possible to effectively distinguish between the three states of alert state, low vigilant state and drowsy state, thus improving the predictive performance of fatigue driving behaviors.